Storms on wind-energy turbine systems: A multi-dimensional architecture for storm-resilient onshore,offshore,and floating wind fleets

Introduction

Storm-induced risks are becoming a critical reliability and design restriction for contemporary wind-energy turbine systems as global implementation expands from onshore terrains and arid regions to deep-water floating arrays and highly interconnected power grids. Intensifying typhoons, hurricanes, extra-tropical windstorms, and convective cells subject turbines, foundations, and networks to compounded wind-wave-rain-sand-lightning conditions that surpass conventional “extreme wind” thresholds, revealing vulnerabilities in structural design, siting practices, and operational planning, especially in rapidly expanding offshore corridors (Nwokolo, 2025). Field and numerical studies of hurricanes and typhoons have demonstrated that storm-specific load paths can induce blade damage, tower buckling, and controller saturation in both fixed-bottom and floating offshore turbines, while also diminishing monopile (Okonkwo et al., 2025e) and soil stiffness and exacerbating mooring-line snap loads in multi-directional seas (Kato et al., 2023; Kim and Manuel, 2016; Yang et al., 2025d). At the basin scale, storm reconstructions in the North Sea and the broader Northwest European shelf, along with case studies like Storm Xaver and Storm Franz, have associated severe cyclones and windstorms with power deficits, curtailment incidents, and infrastructure disruptions, underscoring the vulnerability of established offshore fleets to clustered extreme events (Kettle, 2020, 2023). In typhoon-prone Asian waters, multi-hazard resource and safety assessments demonstrate that combined wind-wave fields and changing cyclone climatology alter energy yields and design parameters for coastal and island systems (Ushie et al., 2025), while long-term fatigue analyses suggest accelerated damage accumulation in cyclone-prone areas (Ning et al., 2025). Concurrent advancements in siting atlases, high-fidelity aero-hydro-servo-elastic modelling, and digital monitoring—including the Global Atlas for extreme turbulence, OpenFAST–OpenFOAM integration, LiDAR- and radar-based storm characterisation, and digital healthcare concepts for ageing jacket structures—illustrate a rapidly expanding toolkit for assessing storm impacts across onshore, fixed-bottom, and floating configurations. However, these developments also expose significant regional and technological biases in the evidence base (Xie et al., 2025). At the system level, probabilistic loss frameworks, studies on firm-capacity diversification, and resiliency-focused operational and planning models highlight the challenges posed by storm sequences to frequency stability, restoration strategies, and adequacy in wind-rich grids (Nwokolo et al., 2023), especially when extreme events coincide with additional climate and environmental stresses (Zare-Bahramabadi et al., 2022). In light of the rapid deployment, changing storm patterns, and inconsistent methodologies, a thorough, storm-focused synthesis that consolidates knowledge across various storm types, installation environments, turbine and system technologies, hazard dimensions, temporal considerations, data sources, and system boundaries is essential for guiding wind-energy turbine systems towards truly storm-resilient design, operation, and policy (Nwokolo et al., 2025a).

The current literature on storms and wind-energy turbine systems has progressed from initial conceptual and design-oriented research to more advanced multi-hazard, multi-scale, and digitally facilitated analyses, yet continues to be disjointed across various technologies, storm categories, and system boundaries. As Manwell et al. (2007) demonstrated, preliminary research on design parameters and survivability for offshore and small-scale turbines highlighted crucial storm-induced loading limitations and guided foundational control mechanisms for rotors and hybrid diesel-wind-PV systems in harsh climates (Okonkwo et al., 2025a, 2025c, 2025d). As shown by Castellani et al. (2015), subsequent research elucidated storm phenomena with improved physical precision, simulating downbursts, microbursts, and hurricane winds on turbine structures, looking at grounding and lightning-induced overvoltages in wind farms, and quantifying extreme wind statistics and storm climatology for site selection and design (Alhousni et al., 2025; Okonkwo et al., 2025b).

In addition to the survivability of the structures, storms also interfere with the energy-conversion and delivery function of wind-energy systems (Yang et al., 2025c). Electricity generation can be reduced by extreme gusts, turbine cut-out, emergency shutdowns, yaw misalignment, wake rearrangement and storm-induced forecast errors, even when turbines are structurally sound (Yang et al., 2025b). These disruptions manifest as limited output, fast power ramps, decreased availability, short-term intermittency, and impaired predictability at wind-farm and fleet scales (Yang et al., 2025a). Studies of offshore farms have revealed that significant ramps in power output are a specific operational hazard, rather than just a secondary structural consequence (Drew et al., 2018). Moreover, changes in power in high-installation-density offshore wind fleets suggest that impacts of storms are transmitted through clustered generation assets and undermine the reliability of the aggregate fleet (Pablo Murcia Leon et al., 2021). Storm-sensitive forecasting is especially critical to system reliability, because neural-network-enhanced wind-power prediction can increase operational anticipation in the Belgian North Sea (van den Bleeken et al., 2025). At the grid level, storm-driven wind variability can increase reserve requirements, reduce firm-capacity contributions, stress unit scheduling, tighten frequency regulation, and complicate restoration planning. Probabilistic projections of extreme wind speeds consequently offer a direct link between storm climatology and unit-commitment decisions (Wu et al., 2022). Moreover, the study of frequency stability in storm conditions reveals the need for resilience measurements that are sensitive to the production beyond the fragility of the component (Das et al., 2020) in wind-rich grids. Firm-capacity diversification studies also find that geographic dispersion impacts sufficiency under windstorm exposure (Bucksteeg, 2019). Thus, this study reformulates storm resilience as a combined structure-production-grid dilemma. This framing integrates atmospheric storm fields, turbine and foundation mechanics, farm-scale energy loss, ramp-rate severity, forecast dependability, curtailment exposure, system adequacy, and restoration planning in a single storms-on-wind-energy architecture.

The advent of floating and offshore structures has shifted attention to mooring snap loads, monopile ringing, seabed response, and aeroelastic behaviour during typhoons and hurricanes. Research by Kim and Manuel (2016); Hsu et al. (2017) illustrates that coupled hydrodynamic-aerodynamic effects can significantly influence storm risk in deep water. As shown by Albani et al. (2018); Bucksteeg (2019), studies have examined the effects of windstorm-driven adequacy and firm capacity for national systems at larger spatial scales, analysed power-output fluctuations in offshore fleets, and linked ENSO and monsoonal variability to wind resource potential. According to Esfahani et al. (2020); Pierella et al. (2021), there have recently been developments in probabilistic frameworks for fragility and loss, storm-resilient distribution planning, and frequency stability during storm conditions, as well as specialised extreme-wave and storm databases and digital sensing initiatives using LiDAR and radar. Notwithstanding this advancement, understanding is predominantly focused on turbine-scale structural-aero-control issues and fixed-bottom offshore locations, while comprehensive analysis of atmospheric storm fields, multi-farm interactions, grid-level resilience, long-term climate variability, and cross-sector enabling systems remains limited. Research by Kareem (2020); Bucksteeg (2019) underscores the necessity for more integrative frameworks that connect engineering specifics with systemic risk.

Recent hybrid energy studies also imply that storm-resilient wind systems should not be assessed solely on the basis of turbine survivability, farm-scale power ramps, or grid-frequency stability. They should also be judged on their ability to enable multi-energy continuity in post-disaster and islanded operation. Sui et al. presented a wind–hydrogen capacity-configuration framework linking electrolyser dynamic efficiency, nonlinear Faraday efficiency, thermal balance, waste-heat recovery, and Wasserstein-distance-based distributionally robust optimisation, demonstrating that a more physically realistic modelling of the electrolyser can cut the planned electrolyser capacity and total investment cost by about 32.3% and 6%, respectively (Sui et al., 2026). This conclusion is crucial for storm-exposed wind systems, because hydrogen can be a resilience buffer if wind power is cut off, unavailable or very erratic. Sui et al. also presented a post-disaster pelagic-island energy-system plan using mobile multi-energy storage to provide power, ice, and water while coordinating diesel generation, cold storage, desalination, network reconfiguration, and islanded-grid merger (Sui et al., 2022). This widens storm resilience from protecting structures to maintaining electricity, preserving food, supplying water and emergency restoration. Tan et al. extended this multi-energy framing by hydro-wind-photovoltaic-hydrogen collaborative dispatch based on ecological value accounting and showed that storm-resilient renewable planning should consider environmental value, hydrogen flexibility, and coordinated dispatch of multiple clean-energy carriers (Tan et al., 2025). Sui et al. also examined pelagic-island microgrid clusters and discovered that noninteger-hour energy transmission can enhance the accuracy of day-ahead scheduling for island clusters in which vessel-based or time-delayed energy transfer challenges the traditional hourly dispatch (Sui et al., 2020). Taken together, these studies offer supporting evidence for the architecture of storms-on-wind-systems to encompass a hybrid multi-energy layer that connects wind generation, hydrogen storage, emergency mobility, desalination, cold-chain preservation, and islanded microgrid repair.

This study addresses these deficiencies by developing a comprehensive framework for storms-on-wind-systems that integrates 102 peer-reviewed publications published between 2005 and 2025 into a cohesive, multi-dimensional database. According to van den Bleeken et al. (2025), the analysis methodically classifies each contribution by technology class, storm type, temporal focus, data source, degree of digitalisation, spatial scale, siting environment, and system boundary, transforming a fragmented corpus into a series of interaction matrices that clarify the study of storms in onshore, fixed-bottom offshore, floating, and system-level contexts. The main objectives are to delineate the evolutionary path from initial design-condition and component resilience research to modern probabilistic (Chukwujindu Nwokolo et al., 2022; Nwokolo et al., 2022a, 2022b), multi-physics (Hassan et al., 2022), and digital-twin frameworks (Hassan et al., 2021); to assess the concentration or absence of storm-specific knowledge across various siting and system-boundary combinations; and to establish a classification of storm impacts that correlates atmospheric hazard regimes with structural fragility, operational variability, and grid-level performance (Benatallah et al., 2024; Nwokolo et al., 2024). This study proposes a storms-on-wind-systems paradigm, which is founded on 102 peer-reviewed publications published between 2005 and 2025. The approach provides a systematic categorisation matrix that translates a fragmented evidence base. Each study is coded according to technology class, storm type, temporal focus, data source, digitalisation level, spatial scale, siting environment and system boundary. The first purpose is to trace the progress of storm-wind research from early survival analyses to probabilistic (Chukwujindu Nwokolo et al., 2022; Nwokolo et al., 2022a, 2022b), multi-physics (Hassan et al., 2022) and digital-twin frameworks (Hassan et al., 2021). The second goal is to find where evidence is concentrated or lacking for siting and system-boundary combinations. The third purpose is to categorise the impacts of storms, relating atmospheric hazards to structural fragility, operational variability and grid-level resilience. The key contribution is the identification of under-explored intersections. These include floating offshore turbines under compound typhoon-wave threats, storm-responsive management of dense offshore wind farms, and integration of field campaigns, reanalysis and numerical models for storm climatology and forecasting. The primary contributions involve elucidating under-researched intersections, including floating offshore turbines facing compounded typhoon and wave hazards, storm-responsive wind farm management in densely clustered offshore environments, and the integration of field campaigns, reanalysis, and numerical models for storm climatology and forecasting, as indicated by Ning et al. (2025); Larsén et al. (2022). The innovation of this work lies in conceptualising storms not only as extreme design factors but also as foundational principles for a multi-scale resilience framework. This framework integrates atmospheric storm fields, turbine and foundation mechanics, wind farm and fleet dynamics, grid and market responses, and cross-sector enabling systems, while incorporating digital and probabilistic methodologies evident in contemporary multi-physics simulations and resilience-focused planning studies, such as those by Campaña-Alonso et al. (2023); Zare-Bahramabadi et al. (2022).

The novelty of this study lies in transforming storms from peripheral extreme-load assumptions into a system-defining architecture for wind-energy resilience. Unlike earlier studies that mainly isolate structural loads, foundation response, mooring behaviour, power forecasting, or distribution-network resilience, this framework integrates storm type, siting environment, turbine technology, hazard dimensionality, temporal horizon, data source, analytical scale, and system boundary within one reproducible 102-study evidence base. This architecture is important because it connects atmospheric storm fields to turbine fragility, offshore foundation degradation, floating-platform dynamics, wind-farm power ramps, forecast uncertainty, grid adequacy, restoration planning, and investment risk. It also exposes under-researched intersections, including typhoon-exposed floating offshore wind, clustered offshore-fleet variability, developing-region cyclone corridors, and storm-driven reliability in highly wind-integrated power systems. Recent studies on tropical-cyclone multi-hazards, offshore-island wind potential, floating-turbine typhoon dynamics, neural-network-enhanced wind-power forecasting, and active-distribution-network resilience show that storm impacts are now evolving from component survivability questions into coupled structure–production–grid challenges (Wen et al., 2024). The importance of this work is therefore anchored in its ability to provide a storm-explicit decision framework for design standards, siting atlases, digital monitoring, probabilistic risk assessment, and resilience-centred energy planning. By organising fragmented evidence into interaction matrices, the study offers a transferable pathway for identifying where storm knowledge is mature, where evidence remains sparse, and where next-generation wind-energy systems require targeted modelling, monitoring, and policy intervention.

This study is designed to provide an advanced and comprehensive analysis of storms impacting wind-energy turbine systems, progressing from evidence collection to integrative synthesis and practical recommendations. The second section outlines the systematic search technique, inclusion-exclusion criteria, and refinement workflow that support the 102-study database, providing a clear methodological foundation for subsequent analyses. The subsequent section delineates the present research landscape in bibliometric and thematic dimensions, whereas the evolutionary stages segment outlines the progression of storm-related wind research from initial survivability investigations to multi-physics, probabilistic, and digital-twin frameworks across onshore, fixed-bottom offshore, floating, and grid-scale parameters. The core analytical sections develop and examine multi-dimensional interaction matrices that connect storm types, siting environments, technology classes, hazard dimensionality, temporal focus, data sources, and system boundaries. Quantitative evaluations of crucial intersections and a classification of storm impacts that connects atmospheric regimes to structural vulnerability, operational variability, and system-level resilience are additional components. The study concludes with a comprehensive discussion that identifies significant knowledge deficiencies and regional or technological oversights, outlines limitations concerning data coverage, modelling assumptions, and transferability, and establishes a prospective research and policy agenda, ultimately presenting succinct conclusions that regard storm-resilient wind-energy turbine systems as a fundamental component of sustainable low-carbon power systems.

Methodology

Search strategy

The Search strategy section is included to ensure methodological transparency, reproducibility and traceability of the evidence base used in this evaluation. It aims to demonstrate how the studies on storms and wind-energy turbine systems were methodically identified within the disciplines of engineering, energy, atmospheric science, offshore structures, climate risk, and power systems. This section also decreases the risk of selection bias by describing the databases, search strings, filters, and theme reasoning utilised to identify relevant research from 2005 to 2025. This is important as the following classification matrices, evolutionary synthesis, quantitative interaction analysis and research-gap mapping rely directly on the quality and breadth of the search methodology. Each database assigned to a particular finding role adds veracity to the 102-study corpus and helps future researchers to duplicate, amend or extend the review. The search technique was meant to identify peer-reviewed articles on storms influencing wind-energy turbine systems from 2005 to 2025, as indicated in Table 1. It was precise, cross-disciplinary and open to new literature. The core engineering and energy-search backbone was established by Scopus and ScienceDirect. They searched combinations of phrases linked to storms, wind systems, and impacts, including structural response, fatigue, fragility, foundations, moorings, floating platforms, aero-servo-elastic behaviour, grid resilience and distribution networks. Web of Science was utilised to expand coverage in atmospheric science, ocean engineering, energy systems and power-system resilience. The topic-based searches allowed for the inclusion of studies where storm-related terminology was used outside the title or keywords. Searches in Taylor & Francis Online and SpringerLink were carried out on specific works in wind engineering, offshore structures, renewable energy, ocean engineering and climate-impact research. These databases improved the coverage of foundation behaviour, aeroelasticity, stochastic loading, structural dynamics and grid-resilience investigations. An additional finding layer was Google Scholar. This helps to uncover current, cross-disciplinary, preprint, thesis and digital-twin-related studies that may not be fully indexed in subscription databases yet (Abdelsalam et al., 2026). All retrieved records were checked by the same inclusion and exclusion rationale. The ultimate search design based on the classification dimensions of the study: storm type, siting environment, technology class, research technique, scale of analysis, hazard dimensionality, temporal focus, data source, and system boundary (Nwokolo et al., 2026; Okonkwo et al., 2026). Table 1 consequently is the backbone of the review reproducibility. It explains the particular database search technique, filtering logic, and rationale utilised to provide a defensible evidence basis for storm-resilient wind-energy turbine systems.

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Table 1.

Database-specific search strategy table tailored to storms on wind-energy turbine systems (2005–2025).

Database	Search queries (2005–2025)	Additional filters	Justification
Scopus	TITLE-ABS-KEY ((“wind turbine*” OR “offshore wind” OR “floating offshore wind” OR “floating wind” OR “wind farm*” OR “wind energy system*”) AND (storm* OR “extreme wind*” OR cyclone* OR typhoon* OR hurricane* OR windstorm* OR thunderstorm* OR “convective storm*”) AND (structur* OR fatigue OR fragility OR foundation* OR monopile* OR “suction bucket” OR mooring OR “floating platform” OR “aero-servo-elastic” OR “power system” OR “distribution network” OR “grid resilien*” OR “extreme event*”))	- Year range: 2005–2025 (inclusive) - Document type: Article, Review, Conference Paper - Language: English - Subject areas: Engineering, Energy, Earth & Planetary Sciences, Environmental Sciences	Serves as the primary discovery backbone for storms on wind-energy turbine systems because it comprehensively indexes the journals and conferences represented in the 102-study evidence base (structural fragility C1, foundations C2, FOWT and moorings C3, aero-servo-elastic control C4, lightning/icing C5, power-system resilience C6, resource climatology C7, and cross-cutting technologies C8). Field-restricted TITLE-ABS-KEY syntax allows reproducible retrieval of studies spanning turbine-scale, foundation, floating-platform, farm, and grid boundaries consistent with the multi-dimensional matrices, and supports export for downstream quantitative classification and sensitivity analysis.
Web of Science	TS=((“wind turbine*” OR “offshore wind” OR “floating wind” OR “floating offshore wind” OR “wind farm*” OR “wind energy system*”) AND (storm* OR “extreme wind*” OR cyclone* OR typhoon* OR hurricane* OR windstorm* OR thunderstorm* OR “convective storm*” OR “winter storm*”) AND (fragility OR “structural integrity” OR “soil–structure interaction” OR “suction bucket” OR monopile* OR mooring OR “floating platform” OR “distribution network” OR “power system”))	- Timespan: 2005–2025 - Document types: Article, Review, Proceedings Paper - Web of Science categories: Engineering, Civil; Engineering, Ocean; Energy & Fuels; Meteorology & Atmospheric Sciences	Complements Scopus by capturing high-impact multidisciplinary journals (e.g., atmospheric science, ocean engineering, power systems) that host key storm–wind papers on extreme wind statistics, metocean design conditions, and probabilistic risk, enriching coverage for classes C1–C7 and the atmospheric-storm and system-boundary categories used in the review. Topic (TS) searching ensures retrieval of cross-disciplinary work where storm language appears in full text rather than only in titles or keywords.
Google Scholar	“storm” OR “cyclone” OR “typhoon” OR “hurricane” OR “windstorm” OR “thunderstorm” AND (“wind turbine” OR “offshore wind” OR “floating offshore wind” OR “floating wind” OR “wind farm” OR “wind energy system”) AND (fragility OR “fatigue damage” OR “foundation displacement” OR “suction bucket” OR mooring OR “snap load” OR “grid resilience” OR “distribution network” OR “power ramp” OR “wind power forecast”)	- Custom range: 2005–2025 - Sort by relevance - Manual screening to retain peer-reviewed journal and conference outputs	Provides a broad, inclusive catch-all layer that can surface grey literature, theses, and preprints on storm impacts on wind-energy systems (e.g., emerging digital-twin frameworks, novel FOWT concepts, cross-sector resilience studies) that may not yet be fully indexed in subscription databases. This layer is particularly useful for capturing enabling or conceptual work (C8) and for cross-checking whether any basin- or technology-specific storm–wind studies are missing from Scopus/Web of Science.
ScienceDirect (Elsevier)	TITLE-ABSTR-KEY ((“wind turbine*” OR “offshore wind” OR “floating offshore wind” OR “floating wind” OR “wind farm*”) AND (storm* OR “extreme wind*” OR cyclone* OR typhoon* OR hurricane* OR windstorm* OR thunderstorm*) AND (fragility OR fatigue OR “soil–structure interaction” OR monopile* OR “suction bucket” OR mooring OR “floating platform” OR “frequency stability” OR “distribution network”))	- Year: 2005–2025 - Article type: Research article, Review - Subject areas: Energy, Engineering, Earth & Environmental Sciences	Targets Elsevier’s flagship energy, engineering, and geoscience portfolio, which hosts many of the techno-economic, structural, metocean, and grid-resilience studies underlying the C1–C7 impact classes. Focusing on TITLE-ABSTR-KEY narrows retrieval to genuinely storm-relevant wind-energy work and supports alignment between the search protocol and the journals most likely to host future cutting-edge contributions on storms and wind-energy turbine systems.
Taylor & Francis Online	(“wind turbine” OR “offshore wind” OR “floating wind” OR “wind farm”) AND (storm OR “extreme wind” OR cyclone OR typhoon OR hurricane OR windstorm OR thunderstorm) AND (foundation OR monopile OR “suction bucket” OR mooring OR “aeroelastic” OR “grid resilience” OR “distribution network”) (search in Title, Abstract, Keywords, Full text)	- Publication date: 2005–2025 - Publication type: Article, Review - Disciplines: Engineering & Technology, Earth & Environmental Science	Captures storm–wind studies in specialist wind engineering, ocean engineering, and natural hazard journals published by Taylor & Francis, including work on stochastic wind loading, structural dynamics, and natural hazards that intersect with C1–C4 and C7 classes. Full-text searching increases sensitivity to articles where storms are treated as case-study drivers of extreme wind or metocean conditions without explicit mention in the title.
SpringerLink	(“wind turbine” OR “offshore wind” OR “floating offshore wind” OR “floating wind” OR “wind farm”) AND (storm OR “extreme wind” OR cyclone OR typhoon OR hurricane OR windstorm OR thunderstorm) AND (fragility OR fatigue OR “soil–structure interaction” OR “floating platform” OR mooring OR “power system” OR “distribution grid” OR “extreme event”) (search in Title + Abstract + Keywords)	- Year: 2005–2025 - Content type: Journal Article, Book Chapter - Subject areas: Energy, Engineering, Earth Sciences, Climate	Ensures coverage of Springer’s wind engineering, renewable energy, and climate-impact titles, which contribute to the resource-climatology, structural, and cross-sector enabling classes (C1, C2, C3, C6, C7, C8). Inclusion of book chapters captures method and benchmark descriptions that underpin the multi-hazard, temporal, and system-boundary classifications in the storm-on-wind-energy turbine systems manuscript.

Full texts in English were used for the final coding step to ensure uniformity, repeatability and comparability throughout the 102-study evidence base. This decision was important since the classification process required the precise extraction of storm type, site environment, turbine technology, research method, hazard dimensionality, temporal horizon, data source, scale of analysis, and system boundary. These variables depend on a consistent interpretation of technical terminology, modelling assumptions and methodological explanations across numerous databases. However, the limitation of language may exclude valuable local studies from typhoon-active locations where national journals, technical reports, and field-monitoring papers may be published in Japanese, Korean, Chinese, or other regional languages. Thus, the restriction to English-language texts should be taken as a constraint of replication rather than as a sign of irrelevance. Future improvements should include a multi-lingual search for sensitivity, including translated phrases like typhoon, offshore wind turbine, floating wind turbine, wind farm, monopile, mooring, storm surge, extreme wind, lightning, and grid resilience. The claim to reproducibility in this review pertains to the bibliographic search, the screening for eligibility, the coding taxonomy and the development of the interaction matrices. That is not to say that all of the raw data sets utilised in the 102 primary research are freely reusable. Some storm–wind research uses proprietary SCADA records, commercial wind-farm operating data, confidential turbine-load measurements, restricted offshore metocean observations or customised numerical setups that are not publicly released. The database should thus be viewed as a replicable evidence-coding database, not as a completely open repository of all underlying field, operational or simulation datasets. To further transparency, each included study should also be categorised for data-accessibility status, code availability, event-catalogue openness, and reproducibility risk.

Study selection and screening

The inclusion and exclusion methodologies in Table 2 jointly create a well-defined yet methodologically robust information base specific to storms affecting wind-energy turbine systems from 2005 to 2025. The inclusion criteria IA1–IA4 necessitate a direct and explicit association between storm or extreme wind events and wind energy assets, while also mandating that this relationship is contextualised within a well-defined system boundary (such as a turbine, foundation and seabed, farm, or grid). Furthermore, it requires that the analysis be quantitatively robust or methodologically transparent, ensuring that each selected study directly informs storm-specific loads, performance, fragility, resilience, or techno-economic insights, rather than general wind or hazard knowledge. IA4 ensures that storms are recognised as genuine hazards instead of mere background weather by mandating explicit storm characterisation, while IA5 limits the corpus to peer-reviewed English-language publications from 2005 to 2025, thereby aligning the database with the emergence of offshore and floating wind technologies and facilitating reproducible coding across multi-dimensional matrices. Conversely, EA1–EA3 consistently exclude research that primarily addresses topics unrelated to wind systems, such as storm analyses devoid of connections to turbines, farms, or grids, or investigations of wind energy that do not consider extreme or multi-hazard conditions, thus maintaining conceptual coherence regarding storm-wind interactions. EA4–EA6 subsequently enhance methodological rigour by omitting non-quantitative commentary, records lacking adequate metadata or full text accessibility, as well as duplicate or superseded versions, ensuring that each of the 102 retained studies constitutes a distinct, fully codable contribution that can be systematically categorised across storm type, siting, technology, method, scale, hazard dimensionality, temporal focus, data source, and system boundary in the ensuing analysis.

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Table 2.

Inclusion (IA1–IA5) and exclusion (EA1–EA6) approaches for the systematic mapping of storms on wind-energy turbine systems (2005–2025).

Code	Approach label	Operational definition (2005–2025)	Justification in the context of storms on wind-energy turbine systems
IA1	Topical relevance to storms and wind-energy turbine systems	Include studies that explicitly analyse storms, extreme winds, or storm-related metocean conditions (e.g., cyclones, typhoons, hurricanes, extra-tropical storms, severe thunderstorms, winter storms) in direct relation to wind turbines, wind farms, or wind-dominated hybrid energy systems.	Ensures that the evidence base directly addresses storm–wind interactions, rather than generic wind energy or generic natural hazards, aligning with the storm-impact classes and storm-type categories used throughout the manuscript.
IA2	System-boundary relevance (turbine, substructure, farm, grid)	Include studies whose primary system boundary is turbine structural–aero–control, support–foundation–seabed and mooring, wind farm/fleet/park, or grid/power-system level where storm conditions are a central driver of loads, performance, or resilience outcomes.	Guarantees coherence with the system-boundary taxonomy (turbine, foundation, farm, grid, cross-sector enabling) and enables multi-scale synthesis.
IA3	Methodological and quantitative contribution	Include quantitative or methodologically explicit contributions: Numerical simulations, probabilistic or risk models, field campaigns, laboratory experiments, data-driven or machine-learning studies, and structured reviews that present clearly defined methods or models for storm impacts on wind systems.	Prioritises work that contributes transferable modelling approaches, datasets, or analytical frameworks, supporting the critical-gap and sensitivity analyses developed in the review.
IA4	Explicit storm-related hazard characterisation	Include studies that provide explicit storm or extreme-hazard characterisation—such as wind-speed exceedance, joint wind–wave–rain fields, lightning or icing loads, storm sequences, or storm-driven power ramps—used to derive design conditions, fragility, fatigue, resilience, or techno-economic impacts for wind systems.	Aligns the corpus with the hazard-dimensionality and temporal-focus classifications, ensuring that selected studies inform storm-specific design loads and resilience metrics rather than generic operational conditions.
IA5	Temporal, linguistic, and publication-quality window	Include peer-reviewed journal articles, conference papers, and scholarly book chapters published in English between 1 January 2005 and 31 December 2025, with sufficient bibliographic information and accessible full text for detailed coding.	Matches the 2005–2025 horizon adopted for the manuscript, captures the emergence of offshore and floating wind technologies, and maintains methodological transparency and reproducibility in the database.
EA1	Non-wind primary technology focus	Exclude studies where the primary focus is non-wind energy technologies (e.g., solar PV, hydropower, transmission lines, buildings, ports) and where wind turbines or farms appear only tangentially or not at all in the storm analysis.	Prevents dilution of the evidence base with non-wind storm impacts, keeping the synthesis sharply focused on wind-energy turbine systems while allowing cross-sector insights to appear only when wind plays a central role.
EA2	Meteorological-only or climate-only without wind-system linkage	Exclude purely meteorological or climatological studies of storms, extreme winds, or climate change that lack explicit linkage to wind-turbine structures, wind farms, or wind-dominated energy systems (e.g., no loads, performance, or risk analysis for turbines or grids).	Avoids inclusion of hazard studies that cannot be mapped onto the system-boundary, impact, or resilience dimensions required for storm–wind synthesis.
EA3	Generic wind-energy studies without storm or extreme-hazard focus	Exclude wind-energy studies that address average resource assessment, generic siting, or routine operations without explicit treatment of storms, extreme winds, or storm-driven multi-hazards.	Ensures that every retained record contributes storm-specific insight, not just general wind-energy knowledge, thereby preserving conceptual consistency with the storm-impact and temporal-focus classifications.
EA4	Non-quantitative policy commentary or editorial content	Exclude editorials, policy notes, opinion pieces, and non-quantitative commentaries that discuss storms, climate change, or wind energy in purely narrative or discursive terms without reproducible methods, data, or models.	Maintains a methodologically robust and data-rich corpus, enabling the multi-dimensional coding (method, hazard, scale, data source, system boundary) and sensitivity analyses set out in the manuscript.
EA5	Insufficient methodological detail, incomplete metadata, or inaccessible full text	Exclude records with insufficient methodological description, missing key metadata (e.g., publication year, venue), or inaccessible full text, where robust coding of storm type, siting, technology, method, hazard dimensionality, temporal focus, or data source is not possible.	Protects against misclassification and bias by ensuring that each included study can be fully and consistently coded across all taxonomic and gap-analysis dimensions.
EA6	Duplicates, superseded versions, and non-unique case studies	Exclude duplicates, superseded preprints, or interim reports when a more complete peer-reviewed version of the same storm–wind study exists, retaining only the most definitive version for coding and analysis.	Prevents oversampling of particular methods, case studies, or results, ensuring that the 102-study database reflects unique contributions to the understanding of storms on wind-energy turbine systems.

Current research status

Figure 2 demonstrates that storms on wind-energy turbine systems has transitioned from a marginal topic to a recognisable research niche with accelerating momentum over 2005–2025. Annual output in panel (a) remains at one to three papers through the late 2000s, then climbs steadily from the mid-2010s and peaks at 16 publications in 2020, with a sustained band of 6–12 papers per year thereafter, mirroring the global expansion of offshore and floating wind and growing concern about extreme-weather resilience. The country distribution in panel (b) reveals a pronounced geographical concentration: China (25 papers) and the United States (24) dominate the field, the United Kingdom contributes a substantial second tier (12 papers), and Denmark, Germany, Japan, Norway and a small group of other industrialised coastal states follow with 3–6 papers each, while many cyclone- and typhoon-exposed countries contribute only one or two studies, highlighting a mismatch between exposure and research capacity. Panel (c) shows that publications are dispersed across a wide range of specialist outlets, with a handful of journals such as Ocean Engineering, Marine Structures, Atmosphere, Renewable Energy and Energy hosting the largest clusters (4–8 papers each), and a long tail of venues with single contributions, indicating that knowledge is fragmented across ocean engineering, wind energy, meteorology and power-system communities rather than consolidated in a single forum. The subject-area profile in panel (d) confirms a strong technical bias: 61 papers are classified under Engineering and 46 under Energy, with secondary representation from Environmental Science (20), Mathematics (13), Earth and Planetary Sciences (12), Materials Science (11) and only minimal contributions from Physics and Astronomy, Computer Science and Chemical Engineering. Taken together, these patterns portray a field that is quantitatively expanding and methodologically diverse, but geographically uneven, publication-wise dispersed, and still dominated by engineering and energy perspectives, underscoring the need for integrative, cross-disciplinary syntheses such as the present review. Figure 3 indicates that storms on wind-energy turbine systems is currently shaped by a small number of institutional, funding, and authorship anchors operating within a broad, diffuse global community, and that the knowledge base is dominated by primary research rather than synthesis. The affiliation profile in panel (3a) shows that a handful of universities and research institutes—led by The University of Texas at Austin and Northeastern University (7 publications each), Norwegian University of Science and Technology (5), and several key Chinese and European universities and laboratories with 3–4 papers apiece—account for a disproportionately large share of outputs, while the majority of other institutions contribute only one or two papers, indicating that deep expertise in storm–wind interactions is concentrated in a few specialised hubs. Funding statistics in panel (3d) reveal an analogous pattern: the National Natural Science Foundation of China alone underpins 15 papers, followed by the Fundamental Research Funds for the Central Universities (7), the U.S. Department of Energy (6), and a cluster of national science foundations, energy agencies, and ocean or structural engineering programmes with 3–5 supported studies each, underscoring that progress in this field is strongly linked to sustained, strategic public investment. Panel (3c) highlights the presence of a small cadre of highly prolific authors with 4–7 contributions, accompanied by a long tail of researchers with one or two publications, which helps explain the emergence of coherent methodological lines around hurricane risk, typhoon-induced loads, floating moorings, and grid resilience alongside fragmentation across less-developed subtopics. Finally, panel (3b) demonstrates that 98% of the corpus consists of full-length research articles, with only 2% classified as review papers, confirming that the field has generated a substantial volume of detailed, case-specific technical studies but very few integrative syntheses that organise this dispersed knowledge into a unified framework for storm-resilient wind-energy turbine systems—precisely the gap that the present manuscript addresses.OPEN IN VIEWER

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Figure 3.

Current research status of the revealed storms on wind-energy turbine systems. (a) Document by affiliation, (b) document by type, (c) document by authorship, and (d) funding sponsors distribution.

Evolutionary stages of storms on wind-energy turbine systems studies

The transition from Section 3 to Section 4 was not merely chronological; it marked a methodological shift from descriptive multi-hazard coupling to quantitative risk, validation, and resilience science. Section 3 established the physical and operational complexity of storm–wind interactions through aero-hydro-servo-elastic simulations, mooring-dynamics models, radar and LiDAR storm observations, SCADA-supported power-ramp analysis, and early firm-capacity studies. However, most Section 3 studies still treated turbine, foundation, farm, and grid interactions in a partially connected manner. Section 4 advanced the field by introducing probabilistic fragility functions, loss-assessment models, resilience-oriented optimisation, frequency-stability metrics, WRF-based storm simulation, curated metocean databases, and experimentally validated foundation-response studies. The mathematical breakthrough was the movement from deterministic load envelopes to conditional probability, exceedance risk, reliability indices, and expected-loss estimation. A representative fragility formulation can be expressed as P ( D S ≥ d I M = x ) , where D S is the damage state and I M is the storm-intensity measure. Similarly, storm-risk valuation can be represented as E ⌊ L ⌋ = ∫ L ( d ) f I M ( x ) d x , linking structural or operational loss to the probability distribution of storm intensity. This transition allowed storms to be assessed not only as isolated load cases, but also as stochastic, multi-scale disruptions affecting foundations, turbines, farm output, distribution networks, and grid frequency (Nwokolo et al., 2025b). Wilkie and Galasso formalised offshore wind-turbine loss and fragility assessment, while Esfahani et al. advanced resilience-oriented distribution-network operation under windstorms. Das et al. extended the transition to power-system frequency stability under storm-driven wind variability, and Larsén et al. strengthened the data backbone through a global atlas of siting parameters. Therefore, Section 4 should be interpreted as the point where storm research became probabilistic, data-enriched, validation-oriented, and operationally relevant for system resilience.

From 2005 to 2025, the 102-study corpus demonstrates a distinct developmental path in storm research related to wind-energy turbine systems, advancing from initial concept definitions and single-hazard turbine analyses to comprehensive, multi-hazard, and grid-aware resilience frameworks, as shown in Table 3. The initial phase (2005–2010) focuses on fundamental design parameters and component-level storm resilience, encompassing research on offshore design conditions and control mechanisms for small turbines and Savonius rotors, variable-pitch systems, and hybrid wind-diesel-PV electrification in extreme climates, addressing concerns regarding the survivability of turbines during storms (Bogue, 2005; Ghosh et al., 2009; Kumar et al., 2010; Manwell et al., 2007; Nagai et al., 2009; Shaahid et al., 2010). The second phase (2011–2015) advances into process-resolved storm loading, encompassing downburst and microburst simulations on turbines, grounding and overvoltage behaviour in wind farms, wave-induced seabed processes, monopile damping and ringing, as well as statistically robust extreme wind estimation and storm characterisation for various climates. This phase signifies a transition from generic “high wind” to specific interactions involving thunderstorms, hurricanes, and wave-structure dynamics (Carswell et al., 2015; Castellani et al., 2015; Martín-Soldevilla et al., 2015; Nguyen et al., 2011, 2013; Thiagarajan et al., 2015). The period from 2016 to 2019 introduces offshore and floating architectures, multi-hazard coupling, and early system-level considerations, encompassing studies on hurricane risk for offshore facilities, mooring snap loads, lightning interactions, electrostatic charging, dual-polarisation radar and LiDAR characterisation of storm fields, typhoon-directional aerodynamic loads, climate-modulated wind resources, power-output fluctuations in offshore farms, and firm-capacity modelling under windstorm risk, thereby connecting storm physics to structural fragility and energy-system adequacy (Albani et al., 2018; Beauchamp and Chandrasekar, 2016; Buchana and McSharry, 2019; Bucksteeg, 2019; Drew et al., 2018; Hsu et al., 2017; Kim and Manuel, 2016; Lian et al., 2016; Méndez et al., 2016). Beginning in 2020, the literature transitions into a probabilistic, digital, and resilience-focused phase, integrating centrifuge and bucket-foundation tests, tuned-mass dampers, storm-sand coupling, LiDAR field campaigns, WRF simulations of deep convection, probabilistic fragility and loss frameworks, risk-based distribution planning, frequency-stability assessments under storm conditions, and curated databases of extreme design waves, while explicitly positioning storms as catalysts for system The most recent studies (2023–2025) consolidate this trend into a multi-physics, multi-hazard, and digital-twin era, with high-fidelity coupling of OpenFAST and OpenFOAM for FOWTs, detailed mooring and soil-degradation analyses under typhoons, field observations of typhoon impacts on monopiles, shared-mooring floating farms, Mars and offshore-island resource assessments, neural-network-enhanced forecasts, storm-sequence case studies such as Storm Franz, distribution-network resilience frameworks, and digital healthcare concepts for ageing jackets in extreme weather, together redefining storm research on wind-energy turbine systems as an integrated enterprise spanning atmospheric hazard characterisation, turbine and foundation dynamics, farm-scale operation, grid resilience, and cross-sector innovation (Campaña-Alonso et al., 2023; Kettle, 2023; Lozon and Hall, 2023; Ning et al., 2025; Shabani et al., 2025; van Den Bleeken et al., 2025; Wen et al., 2024, 2025; Wiley et al., 2025; Xie et al., 2025; Yang et al., 2025d).

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Table 3.

Evolutionary stages of storms on wind-energy turbine systems studies.

Evolutionary stage	Period (years)	Conceptual and methodological signature	Storm–wind themes and system boundaries	Representative examples from the 102-study corpus
Stage 1 – Foundational storm survivability and design conditions	2005–2010	Early work establishes whether wind turbines and small hybrid systems can survive storm environments at all, focusing on design conditions, basic control concepts, and feasibility in harsh climates. Methods are predominantly deterministic structural analyses and techno-economic feasibility studies for onshore or small-scale systems, with storms treated as “extreme wind” envelopes.	Emphasis on turbine structural–aero–control and small hybrid off-grid systems under severe winds; limited hazard dimensionality, primarily single-hazard high-wind events. System boundaries remain local, with minimal consideration of farm or grid-level effects.	Bogue (2005) frames early technology perspectives on wind-related sensing; Manwell et al. (2007) describe offshore design conditions; Ghosh et al. (2009); Nagai et al. (2009) investigate storm security and variable-pitch control; Shaahid et al. (2010) evaluate hybrid wind–PV–diesel systems in harsh desert climates; Kumar et al. (2010) explore low-Reynolds wind turbines for extreme environments.
Stage 2 – Process-resolved storm loading at component and turbine scale	2011–2015	Research shifts toward explicit process-resolved loading, using CFD, aeroelastic modelling, and refined structural analysis to simulate downbursts, microbursts, lightning, and wave-structure interactions. Probabilistic ideas appear, but most studies still emphasise deterministic load envelopes and single turbines.	Themes include extreme gusts, downbursts, lightning, and storm-induced waves, with attention to towers, blades, grounding systems, and seabed processes. System boundaries remain mainly component and single-turbine, but hazard descriptions become more specific (e.g., thunderstorm downbursts, hurricane fields, wave-induced scour).	Castellani et al. (2015) deliver applied statistics for extreme wind estimation; Carswell et al. (2015) investigate wind-turbine grounding and lightning overvoltages; Martín-Soldevilla et al. (2015) refine storm characterisation in different climates; Méndez et al. (2016), early Stage 3) analyse rotor-blade electrostatic charging and lightning vulnerability; Ahmari and Oumeraci (2011) explore wave-induced suspended sediment transport relevant to foundations.
Stage 3 – Offshore, floating, and multi-hazard emergence with early system perspectives	2016–2019	The literature expands to offshore and floating wind, multi-hazard coupling, and first farm- and system-level analyses. Methods combine aero-hydro-servo-elastic simulations, mooring dynamics, metocean statistics, and early probabilistic risk frameworks. Field campaigns, LiDAR, radar, and SCADA data begin to support storm characterisation and power-ramp analysis.	Multi-hazard themes (wind–wave, wind–lightning, wind–rain), mooring snap loads, and farm-scale power ramps emerge, alongside firm-capacity and diversification studies in power systems. System boundaries span turbine, foundation, farm, and regional grids, but couplings between them are still treated in a piecemeal fashion.	Kim and Manuel (2016) assess hurricane risk for offshore plants; Hsu et al. (2017) quantify FOWT mooring tensions and snap loads; Beauchamp and Chandrasekar (2016) use radar to characterise turbine–thunderstorm interactions; Albani et al. (2018) link ENSO and monsoon to long-term wind potential in Malaysia; Drew et al. (2018) analyse power ramps in offshore wind farms; Bucksteeg (2019) studies firm capacity and diversification under windstorms; Buchana and McSharry (2019) investigate high-installation-density offshore fleets.
Stage 4 – Probabilistic, resilience-oriented, and data-enriched storm modelling	2020–2022	Studies increasingly adopt probabilistic, risk-based, and resilience frameworks, linking structural fragility, foundation behaviour, and farm output to system reliability and restoration strategies. Centrifuge and bucket-foundation tests, tuned-mass dampers, WRF-based deep-convection simulations, and curated metocean databases appear, alongside risk-based distribution planning and frequency-stability analysis under storms.	Hazard treatment becomes multi-dimensional (wind–wave, wind–sand, wind–rain), with foundation–seabed, farm, and grid boundaries analysed in an integrated way. Data sources now include reanalysis, campaign datasets, and structured databases of extreme waves, enabling validation and benchmark development.	Furgerot et al. (2020) provide 1 year of hydrodynamic measurements in Alderney Race; Wilkie and Galasso (2020a) formulate a probabilistic loss framework for offshore turbines; Esfahani et al. (2020) develop resiliency-oriented distribution-network operation under windstorms; Letson et al. (2020) use WRF modelling for deep-convection impacts; Kogaki et al. (2020) contribute LiDAR-based complex-terrain winds; Das et al. (2020) study system frequency stability under storm-driven wind variability; Zare-Bahramabadi et al. (2022) propose risk-based resilient planning; Larsén et al. (2022) release the Global Atlas for Siting Parameters.
Stage 5 – Multi-hazard, digital-twin, and cross-sector resilience era	2023–2025	The most recent phase integrates multi-physics, multi-hazard, and digital-twin paradigms with explicit cross-sector resilience perspectives. High-fidelity coupling of aeroelastic and CFD codes, Kalman observers, shared-mooring concepts, and neural network–augmented forecasting are combined with storm-sequence case studies and distribution-network resilience frameworks. Digital-healthcare concepts for ageing structures under extreme weather appear, and planetary analogues broaden thinking about extreme environments.	Storms are now analysed across the full chain from atmospheric fields → FOWT and fixed-bottom structures → farms and fleets → distribution networks and off-grid systems, with attention to compound hazards, climate-modulated regimes, and socio-technical resilience. Cross-sector enabling roles (e.g., celestial batteries, hybrid systems, Mars missions) illustrate how storm-resilient wind systems embed into wider infrastructures.	Campaña-Alonso et al. (2023) demonstrate OpenFAST–OpenFOAM coupling for FOWTs; Lozon and Hall (2023) analyse shared-mooring floating wind farms; Hartwick et al. (2023); Kumar et al. (2010, revisited) consider Mars wind resources; Kettle (2023) documents energy impacts of Storm Franz; Ning et al. (2025) develop KDE-based typhoon-resilient models for offshore islands; Shabani et al. (2025) propose a resilience framework for active distribution networks; van den Bleeken et al. (2025) enhance power forecasts using NWP–NN hybrids; Wen et al., 2025; Wiley et al., 2025; Xie et al., 2025; Yang et al., 2025d advance digital-healthcare, multi-hazard, and structural-control paradigms for turbines and jackets under storms.

This step turned storm–wind research from an early coupled simulation into a probabilistic, verified, and system-aware resilience modelling. Key developments in the field of mathematics were fragility functions, exceedance-probability estimates, expected-loss modelling, reliability indices, stochastic wind speed prediction and risk-based optimisation. Key technology developments included centrifuge testing of suction-bucket foundations, LiDAR field campaigns, WRF-based deep-convection modelling, curated extreme-wave datasets, tuned-mass-damper evaluation, and high-resolution monitoring of storm-sensitive wind conditions. These innovations incorporated foundation behaviour, structural fragility, agricultural production, frequency stability, restoration planning, and distribution-network resilience in a more integrated storm-risk paradigm.

Classification of storms and siting in wind-energy systems

Figure 4 presents a classification scheme that categorises the 102-study corpus along two intersecting axes: storm type and siting type, positioning each contribution based on the predominant climatic driver and the physical context of the wind-energy system. The framework categorises storms as typhoons/tropical cyclones, hurricanes, extra-tropical storms/windstorms, thunderstorms/convective storms, generic storms/extreme events, climate/environmental extremes, and non-storm-specific/enabling research. The research on typhoons and tropical cyclones is prominently illustrated through numerical and field analyses of events in East Asia and the Western Pacific. This includes KDE-based resource assessments for typhoon-prone islands conducted by Ning et al. (2025), multi-physics simulations of super-typhoon impacts on floating platforms by Yang et al., 2025d, and pore-pressure-driven stability analyses of pile-soil systems during extreme storms by Xu et al. (2025). Studies centred on hurricanes, including the hurricane risk assessment for offshore facilities by Kim and Manuel (2016) and the simulated loads from Hurricane Sandy on jacket-supported turbines by Kim and Manuel (2022), establish the Atlantic viewpoint. In contrast, extra-tropical and windstorm events are documented through European storm-impact reconstructions by Kettle (2020, 2023) and offshore windstorm risk modelling in the North Sea by Buchana and McSharry (2019). Nguyen et al. (2011); Nguyen and Manuel (2014); Nguyen et al. (2013) used downburst and microburst load simulations on turbines to represent thunderstorm and convective-storm classes, while Steiger et al. (2018, 2024) used winter thunderstorm field campaigns as examples. Additionally, Albani et al. (2018) examine climate and environmental extremes related to ENSO- and monsoon-modulated wind resources in Malaysia, and O’Neill et al. (2022) investigate the effects of climate variance on multi-trophic coastal systems. Facilitating contributions that are not specific to storms—such as the stochastic and machine-learning advancements in wind engineering articulated by Kareem (2020), the high-fidelity floating offshore wind turbine coupling frameworks developed by Campaña-Alonso et al. (2023), and the global siting-parameter atlases compiled by Larsén et al. (2022)—offer the methodological foundation for integrating storm-specific load and resilience models.OPEN IN VIEWER

The classification along the siting axis differentiates between onshore (including generic), fixed-bottom offshore/general offshore, floating offshore, and system-level/not wind-site-specific configurations, highlighting systematic differences in the interaction of various storm types with turbine technology and scale. Onshore research encompasses structural failure simulations of turbines subjected to high winds in (Chou et al., 2018), the seasonal efficacy of small urban turbines in Rajab et al. (2019), and fragility curves for terrestrial machines equipped with tuned mass dampers under concurrent cyclone and seismic forces in Martín del Campo et al. (2021), emphasising storm susceptibility and mitigation strategies at the individual turbine and small-farm levels. Research on fixed-bottom offshore structures encompasses foundation damping analyses by Carswell et al. (2015), cyclic monopile behaviour under storm-induced loading by Chaloulos et al. (2024), and post-storm soil stiffness degradation surrounding monopiles in clay by Kato et al. (2023). This body of work focuses on monopile and bucket foundations in turbulent shelf seas, while Liu et al. (2023); Sun et al. (2019) offer unique field-based insights into typhoon-induced dynamics for monopile-supported turbines. Floating-offshore classifications encompass a rapidly expanding domain of FOWT research, wherein typhoons and severe storms impact highly compliant systems. This includes dynamic response analyses of innovative floaters conducted by Li et al. (2020); Borg et al. (2024), evaluations of tendon and mooring performance under extreme loading by Wang et al. (2022); Li et al. (2024), as well as the sensitivity of FOWT loads to modelling inputs during idling storms by Wiley et al. (2025). System-level and non-site-specific studies ultimately view storms as catalysts for regional risk and resilience, as seen in the resilience-focused operation of active distribution networks during windstorms in Esfahani et al. (2020), risk-based distribution planning in extreme weather conditions in Zare-Bahramabadi et al. (2022), four-tier resilience frameworks for active distribution networks in Shabani et al. (2025), and firm-capacity diversification model. By cross-classifying each study according to storm type and siting type, the framework illustrated in Figure 4 reveals dense clusters—such as typhoon–floating offshore wind turbine (FOWT) and North Sea windstorm–fixed-bottom combinations—as well as underexplored combinations, including thunderstorm-induced impacts on large floating arrays or climate-change-influenced storm risk in developing offshore corridors, thus offering a systematic overview of current knowledge and research deficiencies for storm-resilient wind energy turbine systems.

Classification of storm wind energy systems

The architecture for storm wind-energy systems depicted in Figure 5 categorises the 102-study database by turbine/system technology and hazard dimensionality, illustrating how particular hardware archetypes are subjected to unique multi-hazard situations. Onshore storm-failure and fragility investigations, including structural collapse models for high winds in Chou et al. (Chou et al., 2018) and cyclone-seismic fragility curves with adjusted mass dampers in Martín del Campo et al. (2021), predominantly focus on land-based utility-scale horizontal axis wind turbines (HAWTs). Numerous studies on monopile and jacket foundations have focused on fixed-bottom offshore turbines, including discussions of foundation damping and dynamic response by Carswell et al. (2015), post-storm clay stiffness degradation by Kato et al. (2023), and typhoon-induced dynamic monitoring by Liu et al. (2023). Snap-load moorings, which Hsu et al. (2017) discussed, innovative floater hydrodynamics, which Li et al. (2020) investigated, advanced aero-hydro-servo-elastic coupling, which Campaña-Alonso et al. (2023) presented, and multi-physics modelling of super-typhoons, which Yang et al., 2025d presented, are all examples of the expanding subclass known as floating offshore wind turbines (systematic and resource-focused investigations, such as typhoon-vulnerable offshore island evaluations by Ning et al. (2025) and ENSO-monsoon-influenced wind potential in Albani et al. (2018), correlate storms with long-term energy accessibility, whereas off-grid hybridisation in Shaahid et al. (2010) and urban micro-turbine efficacy in Rajab et al. (2019) are examples of small-scale and hybrid In low-Reynolds number Martian turbine concepts by Kumar et al. (2010) and Savonius rotor storm protection devices by Ghosh et al. (2009), Vertical Axis Wind Turbines (VAWT) and alternative rotor designs are present. Within these technologies, hazard dimensionality subclasses differentiate climate and environmental extremes, including long-term climate variability as discussed in O’Neill et al. (2022). This differentiation facilitates non-hazard-specific contributions, such as the siting parameter atlas in Larsén et al. (2022), wind-only storm and extreme-wind analyses, including hurricane load simulations by Kim and Manuel (2014), wind-lightning and convective electrification investigations, exemplified by rotor electrostatic charging in Méndez et al. (2016), and winter lake-effect studies in Steiger et al. (2018). Additionally, wind-precipitation and wind-sand interactions have been quantified for typhoon-induced sand transport by Ke et al. (2020), as well as wind-wave coupling within extreme design-wave databases for offshore turbines in Pierella et al. (2021). Multi-hazard resources, including tropical cyclone wind, rain, and wave estimates in Wen et al. (2025), enhance this hazard classification by specifically delineating complex storm causes.OPEN IN VIEWER

A second set of axes illustrates the production of knowledge and the temporal intervals during which storms are examined, integrating research methodology, temporal emphasis, and the level of data source/digitalisation. Conceptual and review contributions include methodological advancements and problems related to ageing assets, encompassing a comprehensive examination of computing, stochastics, and machine learning in wind engineering by Kareem (2020) and a digital healthcare viewpoint on ageing offshore jackets in Xie et al. (2025). In-situ measurements and monitoring constitute a fundamental empirical foundation, encompassing LiDAR-based complex-terrain wind characterisation in Kogaki et al. (2020), Alderney Race hydrodynamics in Furgerot et al. (2020), dynamic response monitoring of typhoon “In-fa” in Liu et al. (2023), and lightning-centric lake-effect campaigns in Steiger et al. (2024). Laboratory and physical-model investigations validate the replication of controlled storm loads, evidenced by centrifuge testing on suction-bucket foundations in Ueda et al. (2020), monopile ringing experiments in Bachynski-Polić et al. (2017), and 1:70-scale Kalman-observer validation in Ammerman et al. (2024). Deterministic modelling encompasses aero-servo-elastic simulations for hurricane and typhoon loads as presented in Kim and Manuel (2016); Lian et al. (2016), hydrodynamics of floating foundations under storm conditions in Li et al. (2020), and the cyclic response of monopiles in Chaloulos et al. (2024). Probabilistic and resilience-focused studies, including cyclone risk assessments for towers in Jaimes et al. (2020), offshore loss and fragility frameworks in Wilkie and Galasso (2020a), and distribution-system resilience planning under extreme weather in Zare-Bahramabadi et al. (2022), present explicit risk metrics. Concurrently, data-driven and machine-learning methodologies enhance forecasts and operations, ranging from ANN-based back-pressure predictions in Du et al. (2011) and extreme-wind probabilistic forecasts for unit scheduling in Wu et al. (2022) to neural-network-augmented North Sea farm forecasts in van den Bleeken et al. (2025). Temporal-focus subclasses illustrate single-storm and short-term events in Hurricane Sandy simulations by Kim and Manuel (2022), super-typhoon load analyses by Xu et al. (2024), historical climatology and extremes in European storm reconstructions by Kettle (2020, 2023), multi-year operational monitoring in offshore-farm ramp studies by Drew et al. (2018), scenario and future-planning in German firm-capacity diversification modelling by Bucksteeg (2019), and cross-temporal methodological syntheses in the WRF deep-convection research of Letson et al. (2020). Data sources encompass conceptual and review articles, field campaigns, experimental setups, and operational SCADA—illustrated by power fluctuations in dense offshore fleets as documented by Pablo Murcia Leon et al. (2021)—as well as purely numerical or NWP/climate atlas products, exemplified by the global siting atlas in Larsén et al. (2022) and typhoon-prone resource modelling in Ning et al. (2025), collectively mapping the field’s advancement towards digitally enabled, twin-style storm analysis.

The last two dimensions—scale of analysis and system boundary—define the extent to which storm impacts are tracked within the energy system, encompassing components from planetary envelopes and atmospheric drivers to cross-sector infrastructures. Component and subsystem scale investigations elucidate the localised behaviour of dampers, blades, and joints, shown by tuned-mass damper comparisons in Lalonde et al. (2020) and atmospheric-ice fracture energy assessments by Pervier and Hammond (2019). Research primarily focuses on single-turbine and structure-scale analyses of structural storm fragility, encompassing offshore monopile dynamics subjected to cyclic loads as discussed in Zha et al. (2022), pile-soil stability influenced by storm-induced pore pressure as examined in Xu et al. (2025), and adaptive blades designed for extreme conditions as presented in Miao et al. (2019). Wind farm and local siting scales are examined in North Sea offshore windstorm risk evaluations by Buchana and McSharry (2019), power ramp characterisation in offshore arrays by Drew et al. (2018), and shared mooring floating farm load analyses by Lozon and Hall (2023). Frequency-stability studies with significant wind contributions are supported by grid, regional, and national-system scales in Das et al. (2020), resilience-focused operation of active distribution networks in Esfahani et al. (2020), four-tier resilience frameworks for networks vulnerable to windstorms in Shabani et al. (2025), and restoration strategies addressing offshore wind risks in Rong et al. (2023). Global and planetary scales are examined through ENSO–monsoon modulation of wind resources in Albani et al. (2018), global siting parameters in Larsén et al. (2022), and Mars wind-resource evaluations by Hartwick et al. (2023). System-boundary classes subsequently correlate the same studies with atmospheric storm-field analysis in Wen et al. (2024), turbine structural-aero-control in Zahran et al. (2015), support-foundation-seabed and mooring assessments in Sharma and Guner (2020); Wang et al. (2022), wind-farm and fleet-level performance in Pablo Murcia Leon et al. (2021), grid and power-system resilience in Zare-Bahramabadi et al. (2022), and cross-sector enabling systems, encompassing hybrid off-grid electrification in Shaahid et al. (2010) to “celestial battery” concepts in Pulkkinen et al. (2009). By systematically cross-referencing technology, hazard dimensionality, methodologies, temporal horizons, scales, data sources, and system boundaries, the classification in Figure 5 transforms a fragmented body of literature into a cohesive framework that elucidates areas where storm impacts on wind-energy systems are thoroughly understood, where multi-scale and multi-hazard connections are weak, and where innovative digital and cross-sector paradigms can propel the next generation of storm-resilient wind-energy research.

Quantitative assessment of the interaction matrix for storm siting across various categories of wind energy turbine systems

Quantitative evaluation of the interaction matrix of storm and siting on wind energy turbine systems

The quantitative interaction matrix between storm type and siting reveals a significantly uneven evidence base, with 57 of 102 studies categorised as “not storm-specific/enabling” across all siting classes: 16 onshore, 18 fixed-bottom offshore, 9 floating offshore, and 14 system-level. This suggests that much of the literature continues to address storms implicitly through generic extreme-load or design envelopes rather than through explicit hazard types, as illustrated in Tables 4 and 5. Among the specified storm categories, research on typhoons and tropical cyclones (13 studies) is notably well-distributed across various locations—four onshore, four fixed-bottom offshore, three floating, and two system-level. This body of work encompasses comprehensive multi-hazard resource and impact assessments for coastal cities in China and the South China Sea (Wen et al., 2025), as well as safety evaluations for fixed-bottom offshore installations in the same region (Wen et al., 2024). Research on thunderstorms and convective storms, totalling 11 studies, predominantly focuses on onshore environments (eight studies). This includes early off-grid hybrid reliability evaluations in Saudi Arabia (Shaahid et al., 2010) and detailed lightning-gust analyses at a wind facility in Greece (Kolokythas et al., 2023). Only system-level initiatives, such as the Ontario lake-effect electrification projects, extend to wider spatial dimensions (Steiger et al., 2018). Research on extra-tropical storms and windstorms (four studies) is predominantly European and offshore, focusing on North Sea events that impact fixed-bottom fleets (Kettle, 2020) and offshore windstorm risk in the same region (Buchana and McSharry, 2019). In contrast, generic storm or extreme-event literature (12 studies) primarily addresses system-level issues and incorporates early fibre-optic sensor methodologies for storm monitoring in the United Kingdom (Bogue, 2005). Classifications of climate and environmental extremes are infrequent (2 studies) and are observed solely at the system level, such as in climate-sensitive waste management scenarios for Hanoi (Schneider et al., 2017), indicating that the integration of long-term climate variability with storm-specific wind-energy risk is just commencing. The siting totals—30 onshore, 29 fixed-bottom offshore, 12 floating offshore, and 31 system-level—indicate that floating technology remains inadequately represented in relation to its vulnerability to severe typhoons and hurricanes. This is evident despite the advent of advanced simulations and mooring studies. Furthermore, the frameworks for system-scale resilience against windstorms in active distribution networks and regional grids are numerically limited when juxtaposed with the escalating operational risks that storms present to highly wind-integrated power systems (Esfahani et al., 2020).

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Table 4.

Quantitative evaluation of the interaction matrix of storm and siting on wind energy turbine systems.

Storm type	Onshore (incl. generic)	Fixed-bottom offshore/general offshore	Floating offshore	System-level/not wind-site specific	Total
Typhoon/tropical cyclone	4	4	3	2	13
Hurricane	0	3	0	0	3
Extra-tropical storm/windstorm	0	2	0	2	4
Thunderstorm/convective storm	8	0	0	3	11
Generic storm/extreme event	2	2	0	8	12
Climate/environmental extremes	0	0	0	2	2
Not storm-specific/enabling	16	18	9	14	57
Total	30	29	12	31	102

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Table 5.

Quantitative evaluation of the interaction matrix of storm and siting on wind energy turbine systems (excluding studies that lack specific/methodological or multi-regional location/country).

Reference	Year	Storm type	Siting	Location/country
van den Bleeken et al. (2025)	2025	Not storm-specific/enabling	Fixed-bottom offshore/general offshore	North Sea, Northwest Europe
Wen et al. (2025)	2025	Typhoon/tropical cyclone	System-level/not wind-site specific	China/South China Sea
Steiger et al. (2024)	2024	Thunderstorm/convective storm	System-level/not wind-site specific	Ontario, Canada
Wen et al. (2024)	2024	Typhoon/tropical cyclone	Fixed-bottom offshore/general offshore	China/South China Sea
Kettle (2023)	2023	Extra-tropical storm/windstorm	System-level/not wind-site specific	Europe (regional)
Hartwick et al. (2023)	2023	Not storm-specific/enabling	Onshore (incl. generic)	Mars (planetary analogue)
Kolokythas et al. (2023)	2023	Thunderstorm/convective storm	Onshore (incl. generic)	Greece
El-Bshah et al. (2021)	2021	Not storm-specific/enabling	Onshore (incl. generic)	Mokha, Yemen
Kettle (2020, 2023)	2020	Extra-tropical storm/windstorm	Fixed-bottom offshore/general offshore	North Sea, Northwest Europe
Furgerot et al. (2020)	2020	Not storm-specific/enabling	Fixed-bottom offshore/general offshore	Alderney Race, English Channel
Bucksteeg (2019)	2019	Not storm-specific/enabling	Onshore (incl. generic)	Germany
Buchana and McSharry (2019)	2019	Extra-tropical storm/windstorm	Fixed-bottom offshore/general offshore	North Sea, Northwest Europe
Albani et al. (2018)	2018	Not storm-specific/enabling	Onshore (incl. generic)	Malaysia
Steiger et al. (2018)	2018	Thunderstorm/convective storm	System-level/not wind-site specific	Ontario, Canada
Schneider et al. (2017)	2017	Climate/environmental extremes	System-level/not wind-site specific	Hanoi, Vietnam
Shaahid et al. (2010)	2010	Thunderstorm/convective storm	Onshore (incl. generic)	Saudi Arabia
Kumar et al. (2010)	2010	Not storm-specific/enabling	Onshore (incl. generic)	Mars (planetary analogue)
Manwell et al. (2007)	2007	Not storm-specific/enabling	Fixed-bottom offshore/general offshore	United States
Bogue (2005)	2005	Generic storm/extreme event	System-level/not wind-site specific	United Kingdom

Quantitative evaluation of the interaction matrix of turbine/system technology and siting matrix on wind energy turbine systems

The quantitative interaction matrix between turbine or system technology and siting indicates that research on wind-energy turbine systems is predominantly focused on land-based utility-scale horizontal-axis wind turbines (HAWTs) and fixed-bottom offshore machines, with 30 studies in the former category and 20 in the latter. Onshore or near-coastal environments represent 53 of 102 records, in contrast to 21 fixed-bottom offshore, 12 floating offshore, and 16 system-level or multi-hazard studies, as shown in Tables 6 and 7. Research on land-based horizontal axis wind turbines (HAWTs) focuses on structural vulnerability, fatigue, and control optimisation in areas susceptible to cyclones and typhoons, as demonstrated by loading assessments from typhoons in coastal China and the South China Sea in Wen et al. (2024), along with fatigue-life evaluations for cyclone-affected regions by Chen et al. (2022). Fixed-bottom offshore studies predominantly concentrate on monopiles and jackets in dynamic shelf seas, integrating foundation damping, soil cyclic degradation, and loading from hurricanes or windstorms, as demonstrated in monopile-stiffness degradation analyses by Kato et al. (2023), fully coupled cyclic monopile simulations in sand by Chaloulos et al. (2024), and hurricane-induced load evaluations on offshore facilities by Kim and Manuel (2016). Floating offshore technology constitutes a diminutive yet swiftly expanding cluster of 13 studies, predominantly within the floating-offshore siting cell. As shown by the OF2 OpenFAST-OpenFOAM coupling in Campaña-Alonso et al. (2023), dynamic TetraSpar floater tests in Borg et al. (2024), and extreme-idling load sensitivity analyses by Wiley et al. (2025), these investigations use multi-physics models, basin experiments, and sensitivity analyses to examine snap loads, slow drift, and mooring-line failure during super typhoons and severe storms System-level and resource-oriented research (16 studies) is focused on the system-level/multi-hazard category, connecting storm regimes to the long-term adequacy and resilience of wind-integrated grids and regions. This includes typhoon-probabilistic resource mapping for offshore islands by Ning et al. (2025), firm-capacity diversification in response to German windstorms by Bucksteeg (2019), and risk-aware restoration strategies for offshore wind in transmission networks by Rong et al. (2023). Cross-sector enabling studies (17 records) predominantly reside in the onshore category, offering campaigns, atlases, and methodological advancements that support design and siting across various technologies Steiger et al. (2018) provided details on lake-effect lightning field experiments in Ontario, Furgerot et al. (2020) reported high-energy tidal and wave measurements in Alderney Race, and Larsén et al. (2022) presented the Global Atlas for extreme winds and turbulence. Only three studies focus on small-scale or hybrid micro-systems and vertical axis wind turbines (VAWT) or alternative rotors. These outliers—spanning from off-grid hybridisation in Saudi Arabia as reported by Shaahid et al. (2010) to low-Reynolds Martian turbines in Kumar et al. (2010)—illustrate the nascent application of storm-resilient concepts to distributed, extraterrestrial, and cross-infrastructure contexts. The subset featuring explicit location coding underscores a significant geographical focus on the North Sea and northwest Europe, the South China Sea, and typhoon-prone Asian coastlines, alongside additional instances in Yemen, Malaysia, and Saudi Arabia, as well as planetary analogues on Mars. This suggests that specific storm-technology-siting combinations are extensively investigated, while others, such as floating arrays in nascent offshore basins or micro-systems in at-risk coastal communities, are notably under-represented in the empirical literature.

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Table 6.

Quantitative evaluation of the interaction matrix of turbine/system technology and siting matrix on wind energy turbine systems.

Turbine/system technology/siting	Onshore/near-coastal turbines	Fixed-bottom offshore	Floating offshore	System-level/multi-hazard	Total
Land-based utility-scale HAWT	29	1	0	0	30
Fixed-bottom offshore	1	19	0	0	20
Floating offshore (FOWT)	1	0	12	0	13
System-level/resource	2	1	0	13	16
Cross-sector enabling	15	0	0	2	17
Small-scale/hybrid micro-systems	2	0	0	1	3
VAWT/alternative rotors	3	0	0	0	3
Total	53	21	12	16	102

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Table 7.

Quantitative evaluation of the interaction matrix of turbine/system technology and siting matrix on wind energy turbine systems (excluding studies that lack specific/methodological or multi-regional location/country).

Authors	Year	Technology	Siting	Location/country
van den Bleeken et al. (2025)	2025	System-level/resource	Onshore/near-coastal turbines	North Sea/Northwest Europe
Wen et al. (2025)	2025	Land-based utility-scale HAWT	Onshore/near-coastal turbines	China/South China Sea
Steiger et al. (2024)	2024	Cross-sector enabling	Onshore/near-coastal turbines	Ontario, Canada
Wen et al. (2024)	2024	Land-based utility-scale HAWT	Onshore/near-coastal turbines	China/South China Sea
Kettle (2023)	2023	System-level/resource	System-level/multi-hazard	Northwest Europe
Hartwick et al. (2023)	2023	System-level/resource	System-level/multi-hazard	Mars (planetary analogue)
Kolokythas et al. (2023)	2023	Land-based utility-scale HAWT	Onshore/near-coastal turbines	Greece
El-Bshah et al. (2021)	2021	System-level/resource	System-level/multi-hazard	Mokha, Yemen
Kettle (2020, 2023)	2020	System-level/resource	System-level/multi-hazard	North Sea/Northwest Europe
Furgerot et al. (2020)	2020	Cross-sector enabling	Onshore/near-coastal turbines	Alderney Race, English Channel
Bucksteeg (2019)	2019	System-level/resource	System-level/multi-hazard	Germany
Buchana and McSharry (2019)	2019	Fixed-bottom offshore	Fixed-bottom offshore	North Sea/Northwest Europe
Albani et al. (2018)	2018	System-level/resource	System-level/multi-hazard	Malaysia
Steiger et al. (2018)	2018	Cross-sector enabling	Onshore/near-coastal turbines	Ontario, Canada
Schneider et al. (2017)	2017	Cross-sector enabling	System-level/multi-hazard	Hanoi, Vietnam
Shaahid et al. (2010)	2010	Small-scale/hybrid micro-systems	System-level/multi-hazard	Saudi Arabia
Kumar et al. (2010)	2010	VAWT/alternative rotors	Onshore/near-coastal turbines	Mars (planetary analogue)
Manwell et al. (2007)	2007	Fixed-bottom offshore	Onshore/near-coastal turbines	United States

Quantitative evaluation of the interaction matrix of research method and siting matrix on wind energy turbine systems

The interaction matrix between research methods and siting indicates a predominance of deterministic numerical modelling, with 49 out of 102 studies employing aero-hydro-servo-elastic or structural simulations. These studies are particularly concentrated in onshore or near-coastal (27 studies) and floating-offshore (11 studies) categories, where multi-physics models analyse typhoon and hurricane loads on towers, floaters, and moorings. Notable works include numerical assessments of typhoon loading on offshore turbines by Lian et al. (2016), hurricane loss and fragility evaluations for monopile farms by Wilkie and Galasso (2020a), and super-typhoon responses of floating offshore wind turbines by Yang et al., 2025d, as detailed in Tables 8 and 9. Statistical, data-driven, and machine-learning methodologies constitute the second largest category, comprising 20 studies, predominantly focused on onshore siting (12 studies). These approaches have been employed for storm-enhanced resource characterisation and ramp forecasting, exemplified by forecasting frameworks in the North Sea and South China Sea developed by van den Bleeken et al. (2025); Wen et al. (2025), as well as in system-level storm-impact reconstructions, including European windstorm energy analyses conducted by Kettle (2020, 2023). Probabilistic, risk, and resilience models (10 studies) primarily focus on fixed-bottom offshore and system-level cells, encompassing cyclone-induced tower risk assessment by Jaimes et al. (2020), offshore windstorm risk modelling in the North Sea by Buchana and McSharry (2019), and risk-based distribution system planning under extreme weather conditions in Zare-Bahramabadi et al. (2022). Field and in-situ campaigns (8 studies) and laboratory or physical-model experiments (4 studies) are relatively limited and predominantly restricted to onshore and fixed-bottom offshore locations, as demonstrated by the Ontario winter lightning campaigns in Steiger et al. (2018), Alderney Race metocean measurements in Furgerot et al. (2020), and centrifuge tests on suction-bucket foundations in Ueda et al. (2020), with an absence of empirical or basin-experiment research focused on floating arrays. Conceptual and review contributions (5 studies) focus on onshore design and multi-hazard frameworks, including early offshore design-condition syntheses by Manwell et al. (2007) and digital healthcare perspectives on ageing jackets by Xie et al. (2025). In contrast, techno-economic and system-level assessments (6 studies) are exclusively conducted at the system level or fixed-bottom scale, correlating storms with adequacy and reliability in varied contexts such as off-grid hybridisation in Saudi Arabia by Shaahid et al. (2010), ENSO-modulated wind resources in Malaysia by Albani et al. (2018), and planetary-analogue wind utilisation on Mars by Hartwick et al. (2023). The subset of studies with explicit geographical coding indicates that sophisticated data-driven and techno-economic methodologies are disproportionately utilised in the North Sea, Northwest Europe, and certain Asian basins, in contrast to cyclone-prone yet data-deficient areas like the Indian Ocean rim and numerous developing coastal nations. This highlights a methodological and spatial disparity that limits the applicability of storm-resilience insights across the global wind-energy sector.

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Table 8.

Quantitative evaluation of the interaction matrix of research method and siting matrix on wind energy turbine systems.

Research method/siting	Onshore/near-coastal turbines	Fixed-bottom offshore	Floating offshore	System-level/multi-hazard	Total
Conceptual, review, or perspective work	4	0	0	1	5
Field/in-situ measurements & monitoring	5	3	0	0	8
Laboratory/physical model experiments	2	2	0	0	4
Numerical modelling/deterministic simulations	27	8	11	3	49
Probabilistic/risk/resilience & planning models	3	4	0	3	10
Statistical, data-driven, or machine-learning methods	12	3	1	4	20
Techno-economic/system-level energy assessment	0	1	0	5	6

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Table 9.

Quantitative evaluation of the interaction matrix of research method and siting matrix on wind energy turbine systems (excluding studies that lack specific/methodological or multi-regional location/country).

Authors	Year	Research method	Siting	Location/country
van den Bleeken et al. (2025)	2025	Statistical, data-driven, or machine-learning methods	Onshore/near-coastal turbines	North Sea/Northwest Europe
Wen et al. (2025)	2025	Statistical, data-driven, or machine-learning methods	Onshore/near-coastal turbines	China/South China Sea
Steiger et al. (2024)	2024	Field/in-situ measurements & monitoring	Onshore/near-coastal turbines	Ontario, Canada
Wen et al. (2024)	2024	Statistical, data-driven, or machine-learning methods	Onshore/near-coastal turbines	China/South China Sea
Kettle (2023)	2023	Statistical, data-driven, or machine-learning methods	System-level/multi-hazard	Northwest Europe
Hartwick et al. (2023)	2023	Techno-economic/system-level energy assessment	System-level/multi-hazard	Mars (planetary analogue)
Kolokythas et al. (2023)	2023	Numerical modelling/deterministic simulations	Onshore/near-coastal turbines	Greece
El-Bshah et al. (2021)	2021	Techno-economic/system-level energy assessment	System-level/multi-hazard	Mokha, Yemen
Kettle (2020, 2023)	2020	Statistical, data-driven, or machine-learning methods	System-level/multi-hazard	North Sea/Northwest Europe
Furgerot et al. (2020)	2020	Field/in-situ measurements & monitoring	Onshore/near-coastal turbines	Alderney Race, English Channel
Bucksteeg (2019)	2019	Techno-economic/system-level energy assessment	System-level/multi-hazard	Germany
Buchana and McSharry (2019)	2019	Probabilistic/risk/resilience & planning models	Fixed-bottom offshore	North Sea/Northwest Europe
Albani et al. (2018)	2018	Techno-economic/system-level energy assessment	System-level/multi-hazard	Malaysia
Steiger et al. (2018)	2018	Statistical, data-driven, or machine-learning methods	Onshore/near-coastal turbines	Ontario, Canada
Schneider et al. (2017)	2017	Numerical modelling/deterministic simulations	System-level/multi-hazard	Hanoi, Vietnam
Shaahid et al. (2010)	2010	Techno-economic/system-level energy assessment	System-level/multi-hazard	Saudi Arabia
Kumar et al. (2010)	2010	Numerical modelling/deterministic simulations	Onshore/near-coastal turbines	Mars (planetary analogue)
Manwell et al. (2007)	2007	Conceptual, review, or perspective work	Onshore/near-coastal turbines	United States

Quantitative evaluation of the interaction matrix of scale of analysis and siting matrix on wind energy turbine systems

The interaction matrix between the scale of analysis and siting demonstrates a robust evidence base for storm-on-wind energy that is predominantly rooted in local and single-structure viewpoints. Of the 102 studies, 39 focus on single-turbine or structure scale, while an additional 17 examine component or subsystem scale, primarily concerning onshore or near-coastal turbines (31 single-turbine and 10 component studies) and fixed-bottom offshore assets (15 single-turbine and 3 component studies). In contrast, comprehensive system-level and planetary perspectives are notably scarce, as illustrated in Tables 10 and 11. Onshore structural research encompasses cyclone vulnerability and tuned-mass damper optimisation in Mexico and analogous terrestrial environments Martín del Campo et al. (2021), thunderstorm downburst loading experiments and simulations in the United States Nguyen et al. (2013), and wind-sand or wind-rain aeroelastic interactions on large turbines in China Ke et al. (2019). In contrast, fixed-bottom offshore structural studies concentrate on ultimate and fatigue limit states of monopiles and jackets subjected to hurricanes and storm conditions, including site-specific fragility assessments in the North Sea Wilkie and Galasso (2020a, 2020b) and post-storm stiffness deterioration of monopiles in clay Kato et al. (2023). Analyses of components and subsystems for floating turbines are limited yet technically advanced, featuring comprehensive investigations of mooring and foundations for innovative floaters and snap-load dynamics during typhoons Li et al. (2024). However, large-scale floating wind farm arrays are notably absent from the framework, highlighting a significant deficiency in comprehending storm-induced wake interactions and fleet-level resilience in deep-water environments. Only 11 studies address grid, regional, and national energy-system scales, predominantly within the system-level/multi-hazard category. These studies examine windstorms as factors influencing adequacy and reliability in systems with significant wind integration, including the firm-capacity effects of German wind diversification Bucksteeg (2019), challenges to frequency stability in large wind-dominated grids due to storms Das et al. (2020), and resilience-focused restoration strategies that co-optimise offshore wind and network recovery Rong et al. (2023). Cross-sector hazard-relevant research (15 studies) broadens the perspective on storm effects on co-located infrastructures and environmental systems, encompassing lightning and lake-effect electrification initiatives in Ontario that directly inform wind-farm vulnerability Steiger et al. (2018) and climate-sensitive waste and aquaculture systems that share storm influences with coastal wind assets O’Neill et al. (2022). Ultimately, global and planetary-scale analyses manifest as a specialised yet conceptually significant category, encompassing multi-basin siting atlases for extreme wind and turbulence classifications Larsén et al. (2022) and Martian wind energy evaluations that rigorously assess turbine designs under drastically different atmospheric conditions Hartwick et al. (2023). This highlights that comprehensive cross-scale integration—from blade and mooring mechanics to farm siting and continental grid resilience—continues to be an evolving rather than fully established aspect of contemporary storm-wind research.

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Table 10.

Quantitative evaluation of the interaction matrix of scale of analysis and siting matrix on wind energy turbine systems.

Scale of analysis/siting	Onshore/near-coastal turbines	Fixed-bottom offshore	Floating offshore	System-level/multi-hazard	Total
Component/subsystem scale	10	3	4	0	17
Cross-sector/non-wind but hazard-relevant scale	8	3	2	2	15
Global/planetary or multi-basin scale	1	0	0	2	3
Grid/regional/national energy-system scale	0	1	0	10	11
Single-turbine/structure scale	21	12	6	0	39
Wind farm/plant/local siting scale	13	2	0	2	17

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Table 11.

Quantitative evaluation of the interaction matrix of scale of analysis and siting matrix on wind energy turbine systems (excluding studies that lack specific/methodological or multi-regional location/country).

Authors	Year	Scale of analysis	Siting	Location/country
van den Bleeken et al. (2025)	2025	Wind farm/plant/local siting scale	Onshore/near-coastal turbines	North Sea/Northwest Europe
Wen et al. (2025)	2025	Wind farm/plant/local siting scale	Onshore/near-coastal turbines	China/South China Sea
Steiger et al. (2024)	2024	Cross-sector/non-wind but hazard-relevant scale	Onshore/near-coastal turbines	Ontario, Canada
Wen et al. (2024)	2024	Wind farm/plant/local siting scale	Onshore/near-coastal turbines	China/South China Sea
Kettle (2023)	2023	Wind farm/plant/local siting scale	System-level/multi-hazard	Northwest Europe
Hartwick et al. (2023)	2023	Global/planetary or multi-basin scale	System-level/multi-hazard	Mars (planetary analogue)
Kolokythas et al. (2023)	2023	Wind farm/plant/local siting scale	Onshore/near-coastal turbines	Greece
El-Bshah et al. (2021)	2021	Grid/regional/national energy-system scale	System-level/multi-hazard	Mokha, Yemen
Kettle (2020, 2023)	2020	Wind farm/plant/local siting scale	System-level/multi-hazard	North Sea/Northwest Europe
Furgerot et al. (2020)	2020	Cross-sector/non-wind but hazard-relevant scale	Onshore/near-coastal turbines	Alderney Race, English Channel
Bucksteeg (2019)	2019	Grid/regional/national energy-system scale	System-level/multi-hazard	Germany
Buchana and McSharry (2019)	2019	Wind farm/plant/local siting scale	Fixed-bottom offshore	North Sea/Northwest Europe
Albani et al. (2018)	2018	Grid/regional/national energy-system scale	System-level/multi-hazard	Malaysia
Steiger et al. (2018)	2018	Wind farm/plant/local siting scale	Onshore/near-coastal turbines	Ontario, Canada
Schneider et al. (2017)	2017	Cross-sector/non-wind but hazard-relevant scale	System-level/multi-hazard	Hanoi, Vietnam
Shaahid et al. (2010)	2010	Grid/regional/national energy-system scale	System-level/multi-hazard	Saudi Arabia
Kumar et al. (2010)	2010	Global/planetary or multi-basin scale	Onshore/near-coastal turbines	Mars (planetary analogue)
Manwell et al. (2007)	2007	Single-turbine/structure scale	Onshore/near-coastal turbines	United States

Quantitative evaluation of the interaction matrix of hazard dimensionality and siting matrix on wind energy turbine systems

The hazard-siting interaction matrix indicates a primarily wind-focused perspective on storms affecting wind-energy turbine systems, with extreme wind conditions solely related to wind comprising 61 of 102 studies. These studies predominantly focus on onshore or near-coastal turbines (26 studies) and fixed-bottom offshore fleets (16 studies), while floating arrays (10 studies) and system-level analyses (9 studies) are only starting to garner similar attention, as illustrated in Tables 12 and 13. Multi-hazard richness is significantly limited: Wind lightning and convective electrification are present in merely 10 studies, predominantly onshore, encompassing comprehensive winter lake-effect campaigns in Ontario and electrification assessments at operational wind facilities in Canada and Greece that delineate lightning–gust co-variability during storm conditions (Steiger et al., 2018; Kolokythas et al., 2023). Wind-precipitation and wind-sand interactions are addressed in five onshore studies that specifically examine rain and sand loading in typhoon conditions, such as in tropical cyclone-induced wind-rain-wave fields over Chinese coastal cities, where multi-hazard signatures are analysed for design and safety evaluation (Wen et al., 2025). Wave-related couplings are limited: merely four records examine wind–wave coupling, and only one study comprehensively addresses fully coupled wind–wave–current metocean loading. Pioneering research on typhoon-induced wind–wave energy flux in the northern South China Sea and North Sea metocean campaigns suggests that authentic tri-axial metocean representation remains an exception rather than a standard in offshore design practice. Climate and environmental extremes constitute a minor yet strategically significant subset of five studies, primarily at the system level, wherein ENSO- and monsoon-influenced wind resources in Malaysia and climate-sensitive solid-waste systems in Hanoi serve as proxies for understanding how changing climate regimes alter storm risk envelopes for adjacent wind infrastructure. The enabling or non-hazard-specific class (16 studies) serves a bridging function by offering atlases, design-condition syntheses, and planetary analogues—such as early US offshore design-condition evaluations and Martian wind-energy analyses—that furnish transferable hazard envelopes without elaborating on specific storm types. The siting totals derived from the matrix (54 onshore/near-coastal, 20 fixed-bottom offshore, 12 floating offshore, and 16 system-level) reveal a pronounced onshore bias in multi-hazard characterisation and indicate a significant deficiency in high-fidelity studies of wind–wave–current and lightning–wave interactions for fixed-bottom and floating offshore farms, despite these assets being increasingly located in some of the most severe cyclone, typhoon, and extra-tropical storm corridors globally.

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Table 12.

Quantitative evaluation of the interaction matrix of hazard dimensionality and siting matrix on wind energy turbine systems.

Hazard dimensionality/siting	Onshore/near-coastal turbines	Fixed-bottom offshore	Floating offshore	System-level/multi-hazard	Total
Climate/environmental extremes	2	0	0	3	5
Enabling/non-hazard-specific	10	2	1	3	16
Wind-only storm/extreme-wind	26	16	10	9	61
Wind/lightning/convective electrification	9	0	0	1	10
Wind–precipitation/wind–sand	5	0	0	0	5
Wind–wave coupling	2	1	1	0	4
Wind–wave–current metocean coupling	0	1	0	0	1

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Table 13.

Quantitative evaluation of the interaction matrix of hazard dimensionality and siting matrix on wind energy turbine systems (excluding studies that lack specific/methodological or multi-regional location/country).

Authors	Year	Hazard dimensionality	Siting	Location/country
van den Bleeken et al. (2025)	2025	Enabling/non-hazard-specific	Onshore/near-coastal turbines	North Sea/Northwest Europe
Wen et al. (2025)	2025	Wind–precipitation/wind–sand	Onshore/near-coastal turbines	China/South China Sea
Steiger et al. (2024)	2024	Wind–lightning/convective electrification	Onshore/near-coastal turbines	Ontario, Canada
Wen et al. (2024)	2024	Wind–wave coupling	Onshore/near-coastal turbines	China/South China Sea
Kettle (2023)	2023	Wind-only storm/extreme-wind	System-level/multi-hazard	Northwest Europe
Hartwick et al. (2023)	2023	Enabling/non-hazard-specific	System-level/multi-hazard	Mars (planetary analogue)
Kolokythas et al. (2023)	2023	Wind–lightning/convective electrification	Onshore/near-coastal turbines	Greece
El-Bshah et al. (2021)	2021	Enabling/non-hazard-specific	System-level/multi-hazard	Mokha, Yemen
Kettle (2020, 2023)	2020	Wind-only storm/extreme-wind	System-level/multi-hazard	North Sea/Northwest Europe
Furgerot et al. (2020)	2020	Enabling/non-hazard-specific	Onshore/near-coastal turbines	Alderney Race, English Channel
Bucksteeg (2019)	2019	Wind-only storm/extreme-wind	System-level/multi-hazard	Germany
Buchana and McSharry (2019)	2019	Wind-only storm/extreme-wind	Fixed-bottom offshore	North Sea/Northwest Europe
Albani et al. (2018)	2018	Climate/environmental extremes	System-level/multi-hazard	Malaysia
Steiger et al. (2018)	2018	Wind–lightning/convective electrification	Onshore/near-coastal turbines	Ontario, Canada
Schneider et al. (2017)	2017	Climate/environmental extremes	System-level/multi-hazard	Hanoi, Vietnam
Shaahid et al. (2010)	2010	Wind–lightning/convective electrification	System-level/multi-hazard	Saudi Arabia
Kumar et al. (2010)	2010	Wind-only storm/extreme-wind	Onshore/near-coastal turbines	Mars (planetary analogue)
Manwell et al. (2007)	2007	Enabling/non-hazard-specific	Onshore/near-coastal turbines	United States

Storm interactions with seismic, thermal, cryogenic, and climate-regime stressors

Multi-hazard classification expanded the study’s current storm-centred taxonomy into a cross-domain stressor architecture that treats storms as coupled atmospheric, geophysical, thermal, cryogenic, material-degradation, and grid-stress events rather than isolated wind extremes. The manuscript already shows that hazard classification remains dominated by wind-only storm conditions, with 61 of 102 studies classified as wind-only, compared with only five climate/environmental-extreme studies and one fully coupled wind–wave–current metocean study. A first subclass should be storm–seismic interaction, covering cases where cyclones, typhoons, or windstorms coincide with earthquake-induced vibration, soil weakening, liquefaction, foundation settlement, or post-seismic fatigue accumulation. This pathway is essential for coastal wind corridors along active tectonic margins, where tower response and foundation rotation may be governed by combined aerodynamic and seismic demand. Cyclone–seismic fragility analysis for land-based turbines provides a direct basis for this subclass by linking wind-induced tower demand with earthquake-sensitive structural vulnerability (Martín del Campo et al., 2021). Suction-bucket and offshore-foundation studies under seismic loading also support this class because storm-driven pore pressure and cyclic foundation response may amplify post-seismic seabed instability by Ueda et al. (2020). A second subclass should be storm–thermal interaction, covering heatwaves, cold waves, rapid thermal cycling, and temperature-driven changes in air density, material stiffness, lubrication, power-electronics cooling, and transformer reliability. Extreme heat may reduce aerodynamic power capture and increase converter derating during storm-related grid stress, while extreme cold may intensify blade brittleness, hydraulic viscosity, pitch-system malfunction, and sensor bias. A third subclass should be storm–icing interaction, where freezing rain, cold storm inflow, and atmospheric ice accretion increase blade mass imbalance, vibration, aerodynamic loss, and fatigue progression. Atmospheric ice fracture studies provide a useful mechanistic bridge between cryogenic surface loading and wind-turbine structural vulnerability by Pervier and Hammond (2019). A fourth subclass should be storm–sand/rain interaction, where typhoon rainfields, desert dust, monsoon outflows, or sand-laden winds erode leading edges, contaminate sensors, modify boundary-layer flow, and increase aerodynamic uncertainty. Wind–sand coupling under strong typhoon conditions demonstrates how particle-laden flow alters aerodynamic force distribution and should be treated as a distinct multi-hazard stressor by Ke et al. (2020). A fifth subclass should be storm–climate-regime interaction, where ENSO, monsoon variability, drought, salinity, corrosion, and long-term climate shifts reshape storm frequency, resource adequacy, maintenance access, and recovery capacity. ENSO–monsoon impacts on Malaysian wind potential show how climate-regime variability can change storm-risk envelopes and long-term energy availability by Albani et al. (2018). This expanded classification therefore converts multi-hazard analysis into a structure–environment–production–grid framework for storm-resilient wind systems.

Quantitative evaluation of the interaction matrix of temporal-focus and siting matrix on wind energy turbine systems

The temporal-focus × siting interaction matrix reveals a predominantly method-centric yet temporally disparate evidence base, with cross-temporal or methodological studies constituting nearly half of all records (51 of 102). These studies encompass 28 onshore or near-coastal sites, 12 fixed-bottom offshore locations, eight floating offshore applications, and merely three system-level or multi-hazard syntheses. This distribution aligns with atlas-type design frameworks and databases, such as the Global Atlas for Siting Parameters and DeRisk, which compile extreme wind data across various basins and turbine classes, as demonstrated in Tables 14 and 15. Analyses of single-storm or short-term events constitute the second largest category, comprising 26 studies. Onshore and near-coastal case studies of individual typhoons, winter storms, and convective systems that examine turbine or foundation responses during specific events are what define this category the most. Notable examples include the meticulous tracking of typhoon-induced vibrations in Chinese offshore monopiles and case studies from the North Sea and northwest Europe that correlate metocean conditions with fleet-scale power and infrastructure impacts. Lake-effect winter campaigns in Ontario and neural-network-augmented offshore forecasts in the Belgian North Sea serve as examples of how sub-seasonal operational variability is relatively specialised, with seven studies—six onshore and one fixed-bottom offshore—examining high-frequency ramping, lightning-gust co-variability, and mesoscale convection relevant today-to-week operational forecasting (Steiger et al., 2018). Historical climatology and extremes (six studies) divided into onshore and system-level perspectives: Long-term assessments of wind resources influenced by ENSO and monsoon patterns in Malaysia, along with preliminary reviews of US offshore design conditions, establish climatic envelopes for storm-class design. However, these envelopes seldom translate directly into comprehensive turbine-scale risk mappings for modern floating or deep-water assets. Multi-year operational monitoring is limited, comprising only five studies, with a few onshore and fixed-bottom locations, such as Alderney Race, providing continuous metocean data that could support comprehensive fatigue and resilience calibration, yet are presently underutilised in multi-hazard design frameworks (Furgerot et al., 2020). Scenario and future-oriented planning occupies a minor yet strategically significant niche within seven studies, predominantly at the system level, where techno-economic planning for hybrid diesel-wind systems, geographically diversified wind fleets, or planetary analogues on Mars incorporates storm risk into long-term capacity and reliability decisions, without addressing the intricate dynamics of turbines amid evolving storm statistics. The matrix indicates a significant disparity: The extensive cross-temporal and single-event data for onshore assets is juxtaposed with the scant long-term monitoring, historical climatology, and future-scenario analyses for both floating and fixed-bottom offshore sites. This suggests that the design and operation of next-generation offshore fleets predominantly depend on extrapolated or methodological frameworks rather than authentic longitudinal storm experience at the hardware level.

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Table 14.

Quantitative evaluation of the interaction matrix of Temporal-focus and siting matrix on wind energy turbine systems.

Temporal focus/siting	Onshore/near-coastal turbines	Fixed-bottom offshore	Floating offshore	System-level/multi-hazard	Row total
Cross-temporal/methodological	28	12	8	3	51
Historical climatology/extremes	3	0	0	3	6
Multi-year operational monitoring	2	3	0	0	5
Scenario/future-oriented planning	1	0	0	6	7
Single-storm/short-term event	14	4	4	4	26
Sub-seasonal operational variability	6	1	0	0	7

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Table 15.

Quantitative evaluation of the interaction matrix of Temporal-focus and siting matrix on wind energy turbine systems (excluding studies that lack specific/methodological or multi-regional location/country).

Authors	Year	Temporal focus	Siting	Location/country
van den Bleeken et al. (2025)	2025	Sub-seasonal operational variability	Onshore/near-coastal turbines	North Sea/Northwest Europe
Wen et al. (2025)	2025	Single-storm/short-term event	Onshore/near-coastal turbines	China/South China Sea
Steiger et al. (2024)	2024	Sub-seasonal operational variability	Onshore/near-coastal turbines	Ontario, Canada
Wen et al. (2024)	2024	Single-storm/short-term event	Onshore/near-coastal turbines	China/South China Sea
Kettle (2023)	2023	Single-storm/short-term event	System-level/multi-hazard	Northwest Europe
Hartwick et al. (2023)	2023	Scenario/future-oriented planning	System-level/multi-hazard	Mars (planetary analogue)
Kolokythas et al. (2023)	2023	Sub-seasonal operational variability	Onshore/near-coastal turbines	Greece
El-Bshah et al. (2021)	2021	Cross-temporal/methodological	System-level/multi-hazard	Mokha, Yemen
Kettle (2020, 2023)	2020	Single-storm/short-term event	System-level/multi-hazard	North Sea/Northwest Europe
Furgerot et al. (2020)	2020	Multi-year operational monitoring	Onshore/near-coastal turbines	Alderney Race, English Channel
Bucksteeg (2019)	2019	Scenario/future-oriented planning	System-level/multi-hazard	Germany
Buchana and McSharry (2019)	2019	Single-storm/short-term event	Fixed-bottom offshore	North Sea/Northwest Europe
Albani et al. (2018)	2018	Historical climatology/extremes	System-level/multi-hazard	Malaysia
Steiger et al. (2018)	2018	Sub-seasonal operational variability	Onshore/near-coastal turbines	Ontario, Canada
Schneider et al. (2017)	2017	Cross-temporal/methodological	System-level/multi-hazard	Hanoi, Vietnam
Shaahid et al. (2010)	2010	Scenario/future-oriented planning	System-level/multi-hazard	Saudi Arabia
Kumar et al. (2010)	2010	Cross-temporal/methodological	Onshore/near-coastal turbines	Mars (planetary analogue)
Manwell et al. (2007)	2007	Historical climatology/extremes	Onshore/near-coastal turbines	United States

Quantitative evaluation of the interaction matrix of data source/digitalization level and siting matrix on wind energy turbine systems

The quantitative assessment of the relationship between data source/digitalization level and siting indicates a research environment where high-level conceptual and review studies predominate in wind energy turbine systems, whereas genuinely digital, data-intensive evidence is limited and unevenly allocated across siting categories, as illustrated in Tables 16 and 17. Conceptual and outlook studies comprise approximately 49% of the total corpus (49 out of 102), with a significant concentration on onshore or near-coastal turbines (30 studies) and a notable, albeit lesser, representation of fixed-bottom offshore, floating offshore, and system-level or multi-hazard perspectives. This distribution indicates a predominant reliance on synthesised storm climatologies, qualitative resilience frameworks, and scenario narratives, rather than direct data streams from turbines or farms. Conversely, a limited segment of field campaigns and in-situ monitoring (8 studies) alongside laboratory or experimental rig investigations (5 studies) underpins the empirical characterisation of storm-turbine interaction, with these endeavours predominantly restricted to onshore and fixed-bottom offshore environments, including lake-effect lightning and electrification measurements in Ontario and vigorous tidal races in the English Channel. Operational SCADA or high-frequency turbine traces are present only twice in the entire matrix, highlighting a significant digitalisation deficiency at the asset level, particularly for floating offshore systems, where no study in the sample utilises SCADA, laboratory rigs, or field campaigns. Conversely, purely numerical and synthetic studies constitute the second principal pillar (32 of 102), encompassing all siting categories and facilitating virtual experimentation with extreme winds, coupled metocean conditions, and fleet-level performance under hypothetical or probabilistic storm scenarios, including system-level adequacy and diversification analyses for extensive North Sea portfolios and forward-looking evaluations for extraterrestrial analogues such as Mars. Reanalysis, numerical weather prediction, and climate atlas products (6 studies) are primarily utilised for onshore and system-level configurations, aiding in long-term siting decisions and hazard classification through ENSO-modulated wind climatologies in Malaysia, storm-environment reconstruction in Northwest Europe, and urban-scale waste-climate interactions in rapidly developing areas. The filtered location-specific subset in the interaction matrix reveals a significant geographical concentration around the North Sea, South China Sea, and select Atlantic and tidal channels, alongside a nascent yet constrained representation of arid grid-extension contexts in Saudi Arabia and vulnerable coastal systems in Yemen and Vietnam, suggesting that digital data infrastructures and sophisticated numerical pipelines are disproportionately advanced in well-instrumented basins. The matrix defines a stratified digitalization hierarchy in which a thorough synthesis of empirical, reanalysis, and numerical data augments onshore and near-coastal turbines. Fixed-bottom offshore applications predominantly depend on synthetic simulations with limited field validation, while floating offshore designs are nearly entirely virtualised. Furthermore, system-level or multi-hazard analyses utilise reanalysis and synthetic ensembles to assess storm risk across power generation infrastructure.

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Table 16.

Quantitative evaluation of the interaction matrix of data source/digitalization level and siting matrix on wind energy turbine systems.

Data source/digitalisation level/siting	Onshore/near-coastal turbines	Fixed-bottom offshore	Floating offshore	System-level/multi-hazard	Total
Conceptual/review/outlook	30	7	6	6	49
Field campaigns/in-situ monitoring	5	3	0	0	8
Laboratory/experimental rigs	3	2	0	0	5
Operational SCADA/high-frequency traces	1	1	0	0	2
Purely numerical/synthetic	12	7	6	7	32
Reanalysis/NWP/climate atlases	3	0	0	3	6

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Table 17.

Quantitative evaluation of the interaction matrix of data source/digitalization level and siting matrix on wind energy turbine systems (excluding studies that lack specific/methodological or multi-regional location/country).

Authors	Year	Data source/digitalisation level	Siting	Location/country
van den Bleeken et al. (2025)	2025	Reanalysis/NWP/climate atlases	Onshore/near-coastal turbines	North Sea/Northwest Europe
Wen et al. (2025)	2025	Conceptual/review/outlook	Onshore/near-coastal turbines	China/South China Sea
Steiger et al. (2024)	2024	Field campaigns/in-situ monitoring	Onshore/near-coastal turbines	Ontario, Canada
Wen et al. (2024)	2024	Purely numerical/synthetic	Onshore/near-coastal turbines	China/South China Sea
Kettle (2023)	2023	Conceptual/review/outlook	System-level/multi-hazard	Northwest Europe
Hartwick et al. (2023)	2023	Purely numerical/synthetic	System-level/multi-hazard	Mars (planetary analogue)
Kolokythas et al. (2023)	2023	Conceptual/review/outlook	Onshore/near-coastal turbines	Greece
El-Bshah et al. (2021)	2021	Purely numerical/synthetic	System-level/multi-hazard	Mokha, Yemen
Kettle (2020, 2023)	2020	Conceptual/review/outlook	System-level/multi-hazard	North Sea/Northwest Europe
Furgerot et al. (2020)	2020	Field campaigns/in-situ monitoring	Onshore/near-coastal turbines	Alderney Race, English Channel
Bucksteeg (2019)	2019	Purely numerical/synthetic	System-level/multi-hazard	Germany
Buchana and McSharry (2019)	2019	Purely numerical/synthetic	Fixed-bottom offshore	North Sea/Northwest Europe
Albani et al. (2018)	2018	Reanalysis/NWP/climate atlases	System-level/multi-hazard	Malaysia
Steiger et al. (2018)	2018	Conceptual/review/outlook	Onshore/near-coastal turbines	Ontario, Canada
Schneider et al. (2017)	2017	Reanalysis/NWP/climate atlases	System-level/multi-hazard	Hanoi, Vietnam
Shaahid et al. (2010)	2010	Purely numerical/synthetic	System-level/multi-hazard	Saudi Arabia
Kumar et al. (2010)	2010	Conceptual/review/outlook	Onshore/near-coastal turbines	Mars (planetary analogue)
Manwell et al. (2007)	2007	Conceptual/review/outlook	Onshore/near-coastal turbines	United States

Quantitative evaluation of the interaction matrix of system boundary and siting matrix on wind energy turbine systems

The quantitative interaction matrix between system boundaries and siting indicates that research on storms affecting wind energy has primarily centred on turbine-focused studies at onshore and near-coastal locations. Structural-aero-control investigations constitute the predominant cluster, typically emphasising high-wind behaviour, aeroelastic responses, and control strategies under extreme loading conditions. Studies on blade dynamics, controller performance, and yaw-pitch interactions during strong winds and downbursts reported by Zahran et al. (2015), as detailed in Tables 18 and 19, serve as examples of this. In offshore environments, the system boundary descends into the support-foundation-seabed and mooring domain, where monopile ringing, suction-bucket response, soil stiffness degradation, and extreme mooring tensions prevail, illustrating the offshore focus of studies such as Ueda et al. (2020); Kato et al. (2023); Carswell et al. (2015); Bachynski-Polić et al. (2017); Hsu et al. (2017). Letson et al. (2020); Steiger et al. (2018); Albani et al. (2018) have shown that research on atmospheric storm fields and resource environments is relatively scarce but strategically important because it uses mesoscale and field campaign data to connect convective storms, hail, lake-effect lightning, and large-scale climate phenomena like ENSO to localised wind resource and hazard characterisation. As shown by Wu et al. (2022); Esfahani et al. (2020); Das et al. (2020), system-level and multi-hazard boundaries are most advanced in grid and power-system siting, where probabilistic forecasts of storm-induced wind speeds, resilience-focused operations, and frequency-stability assessments under high wind penetration convert local turbine and farm effects into network-scale risk metrics. Cross-sector and enabling-system studies integrate storm-risk insights into comprehensive energy-transition or infrastructure narratives, connecting spatial diversification, market reliability, and climate-sensitive planning across various fleets and regions, as demonstrated in Bucksteeg (2019); Buchana and McSharry (2019); Kareem (2020). The matrix collectively underscores a developed yet fragmented body of literature, characterised by comprehensive offshore structural and foundation modelling, extensive onshore turbine-level storm mechanics, and nascent but limited integrative research at the wind farm and multi-sector system interface. This suggests a necessity for future methodologies that explicitly integrate atmospheric extremes, structural responses, grid resilience, and cross-sector cascading effects within a cohesive storms-on-wind-systems framework.

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Table 18.

Quantitative evaluation of the interaction matrix of system boundary and siting matrix on wind energy turbine systems.

System boundary/siting	Onshore/near-coastal turbines	Fixed-bottom offshore	Floating offshore	System-level/multi-hazard	Total
Atmospheric storm field/resource environment	8	2	0	5	15
Cross-sector/enabling systems	11	0	0	5	16
Grid/power-system level	0	0	0	6	6
Support–foundation–seabed & mooring	3	9	6	0	18
Turbine structural–aero–control	24	7	6	0	37
Wind farm/fleet/park	8	2	0	0	10

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Table 19.

Quantitative evaluation of the interaction matrix of system boundary and siting matrix on wind energy turbine systems (excluding studies that lack specific/methodological or multi-regional location/country).

Authors	Year	System boundary	Siting	Location/country
van den Bleeken et al. (2025)	2025	Wind farm/fleet/park	Onshore/near-coastal turbines	North Sea/Northwest Europe
Wen et al. (2025)	2025	Atmospheric storm field/resource environment	Onshore/near-coastal turbines	China/South China Sea
Steiger et al. (2024)	2024	Atmospheric storm field/resource environment	Onshore/near-coastal turbines	Ontario, Canada
Wen et al. (2024)	2024	Atmospheric storm field/resource environment	Onshore/near-coastal turbines	China/South China Sea
Kettle (2023)	2023	Cross-sector/enabling systems	System-level/multi-hazard	Northwest Europe
Hartwick et al. (2023)	2023	Atmospheric storm field/resource environment	System-level/multi-hazard	Mars (planetary analogue)
Kolokythas et al. (2023)	2023	Wind farm/fleet/park	Onshore/near-coastal turbines	Greece
El-Bshah et al. (2021)	2021	Atmospheric storm field/resource environment	System-level/multi-hazard	Mokha, Yemen
Kettle (2020, 2023)	2020	Cross-sector/enabling systems	System-level/multi-hazard	North Sea/Northwest Europe
Furgerot et al. (2020)	2020	Cross-sector/enabling systems	Onshore/near-coastal turbines	Alderney Race, English Channel
Bucksteeg (2019)	2019	Grid/power-system level	System-level/multi-hazard	Germany
Buchana and McSharry (2019)	2019	Wind farm/fleet/park	Fixed-bottom offshore	North Sea/Northwest Europe
Albani et al. (2018)	2018	Atmospheric storm field/resource environment	System-level/multi-hazard	Malaysia
Steiger et al. (2018)	2018	Atmospheric storm field/resource environment	Onshore/near-coastal turbines	Ontario, Canada
Schneider et al. (2017)	2017	Cross-sector/enabling systems	System-level/multi-hazard	Hanoi, Vietnam
Shaahid et al. (2010)	2010	Grid/power-system level	System-level/multi-hazard	Saudi Arabia
Kumar et al. (2010)	2010	Turbine structural–aero–control	Onshore/near-coastal turbines	Mars (planetary analogue)
Manwell et al. (2007)	2007	Turbine structural–aero–control	Onshore/near-coastal turbines	United States

Classification of storm impact on wind-energy systems

An eight-class taxonomy based on specialised research in foundations, aerodynamics, atmospheric electrification, grid resilience, and enabling technologies supports the quantitative interaction map of storm impacts on wind energy systems, which is primarily focused on structural, floating platform, and resource-siting dimensions. The corpus is categorised into eight impact classes, as detailed in Table 20 and Figure 6, comprising 15 studies in C1, C3, and C7; 14 in C4; 13 in C8; and 10 in C2, C5, and C6, indicating a research focus on structural-hydrodynamic response and storm climatology compared to foundation, electrical, and atmospheric-electrical hazards. Cyclone fatigue modelling and hurricane load simulations for jacket and monopile systems in coastal areas indicate that structural integrity and fragility analyses in C1 are paramount for turbine-scale risk assessment. These studies quantify ultimate and fatigue limit states for towers and support structures subjected to cyclone and hurricane forces, often employing probabilistic frameworks that incorporate storm-induced vibrations and site-specific hazard characterisation. Research on foundation and soil–structure interaction in C2 addresses storm-induced pore-pressure accumulation, stiffness deterioration, and lateral movement of monopiles and suction buckets in sandy and clayey substrates, enhancing resilience-focused design of seabed support under cyclic storm conditions (Kato et al., 2023). Floating platforms and mooring responses in C3 constitute a substantial cluster, wherein multi-physics simulations and basin experiments on spars, semi-submersibles, and innovative floaters elucidate coupled aero-hydro-servo dynamics, snap loads, and mooring failure envelopes during super-typhoons and severe extratropical storms (Yang et al., 2025d). Aerodynamic and aero-servo-elastic studies categorised under C4 concentrate on unsteady blade loading, downburst and typhoon inflows, as well as sophisticated pitch/maximum power point tracking (MPPT) and damping strategies, demonstrating that enhanced controllers and tuned-mass dampers can mitigate storm-induced instabilities while maintaining energy capture. C5 emphasises the significance of lightning, icing, hail, and electromagnetic effects at the atmosphere–turbine interface, where winter lightning campaigns, ice fracture assessments, and overvoltage simulations collectively underscore the necessity for enhanced grounding, blade protection, and radar compatibility in areas susceptible to storms. C6 correlates storms with the resilience of power systems and wind farms at a systemic level, illustrating through research on wind-ramp statistics, unit commitment, frequency stability, and risk-informed restoration that storm events can transmit from individual turbines to large-scale power systems without effective operational planning. Resource assessment and siting studies in C7 finalise the physical hazard loop by developing storm-dominated wind climates, extreme-wind statistics, and ENSO/monsoon-modulated potentials for coastal, island, and complex-terrain locations, thereby directly influencing turbine class selection and design load envelopes for storm-exposed projects. C8 encompasses interdisciplinary and facilitative endeavours, including Mars wind-resource evaluations, hybrid energy system analyses, and climate impact studies, as well as cutting-edge sensing technologies and methodological advancements in wind engineering. This comprehensive approach establishes a wider technological and climatic framework essential for developing genuinely storm-resilient wind energy infrastructure across various scales.

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Table 20.

Classification of storm impact on wind-energy systems.

Code	Impact class (primary storm impact on wind-energy systems)	Dominant subsystem scale	No. of studies	Focus	References
C1	Structural integrity, fatigue and fragility of turbines under cyclones, hurricanes and strong winds	Rotor–nacelle–tower, jacket/monopile superstructure, and overall turbine structural system	15	Ultimate and fatigue limit states of towers and support structures, storm-induced vibrations, and probabilistic asset risk under cyclone, hurricane and strong-wind loading	(Buchana and McSharry, 2019; Chen et al., 2022; Chou et al., 2018; Jaimes et al., 2020; Liu et al., 2023; Martín del Campo et al., 2021; Sun et al., 2019; Xie et al., 2025)
C2	Foundations, seabed processes and soil–structure interaction under storm and cyclic loading	Monopiles, suction buckets, pile groups, seabed sediments	10	Storm-driven pore-pressure build-up, stiffness degradation, monopile displacement and dynamic response of foundations (including cyclic storm and, in one case, seismic loading as a multi-hazard analogue).	(Bachynski-Polić et al., 2017; Carswell et al., 2015; Chaloulos et al., 2024; Kato et al., 2023; Sharma and Guner, 2020; Ueda et al., 2020; Xu et al., 2025; Zha et al., 2022)
C3	Floating platforms and mooring response in combined wind–wave storm seas	Spar/semi-sub/TLP floaters, mooring lines, tendons, and coupled aero-hydro-servo dynamics	15	Multi-physics dynamics of floating offshore wind turbines (FOWTs), floater motions, mooring-line forces, snap loads and storm-scale wave fields.	(Borg et al., 2024; Campaña-Alonso et al., 2023; Hsu et al., 2017; Li et al., 2020, 2024; Pierella et al., 2021; Wang et al., 2022; Wiley et al., 2025; Xu et al., 2024; Yang et al., 2025d)
C4	Aerodynamics, aero-servo-elastic response and storm-oriented control	Blades, pitch/torque control, aeroelastic DOFs and tuned-mass dampers.	14	Unsteady aerodynamics under typhoons, downbursts and high-turbulence regimes, as well as advanced control (pitch, MPPT, damping) to maintain stability and storm security.	(Ammerman et al., 2024; Ghosh et al., 2009; Ke et al., 2020; Lalonde et al., 2020; Lian et al., 2016; Miao et al., 2019; Nguyen et al., 2011; Nguyen et al., 2013; Zahran et al., 2015)
C5	Lightning, icing, hail and electromagnetic storm impacts	Turbine blades (icing, charging), lightning protection, grounding systems, radar/EM environment	10	Atmospheric electrification, lightning attachment and overvoltages, winter thunderstorm structure, ice accretion fracture and hail microphysics as they affect turbine safety and grid interfaces.	(Kolokythas et al., 2023; Letson et al., 2020; Méndez et al., 2016; Pervier and Hammond, 2019; Steiger et al., 2018; Steiger et al., 2024)
C6	Power-system, wind-farm and distribution-grid resilience to windstorms	Multi-turbine fleets, distribution networks, bulk power systems and operational planning	10	How storms propagate through the energy system: Wind-power ramps, frequency stability, unit commitment, restoration strategies and risk-aware planning of distribution networks.	(Bucksteeg, 2019; Das et al., 2020; Drew et al., 2018; Esfahani et al., 2020; Pablo Murcia Leon et al., 2021; Rong et al., 2023; Shabani et al., 2025; Wu et al., 2022; Zare-Bahramabadi et al., 2022)
C7	Resource assessment, siting, extreme-wind climatology and storm forecasting	Atmosphere–turbine interface at site scale, including metocean design conditions and storm climatology	15	Characterising storm-dominated wind climates (typhoons, winter storms, ENSO, monsoon), extreme-wind/turbulence statistics, ramp-risk climatology and siting classes for storm-exposed turbines.	(Ning et al., 2025; van den Bleeken et al., 2025; Wen et al., 2024, 2025; Kettle, 2023; Larsén et al., 2022; El-Bshah et al., 2021; Kettle, 2020, 2023, 2023; Kogaki et al., 2020; Rajab et al., 2019; Albani et al., 2018; Castellani et al., 2015; Martín-Soldevilla et al., 2015; Manwell et al., 2007)
C8	Cross-cutting/enabling technologies and contextual climate impacts	Enabling methods, alternative energy-conversion concepts, climate-change context and sensing technologies that indirectly strengthen storm-resilient wind-energy design	13	Studies not purely storm-specific but that enable storm-resilient wind systems, including conceptual wind-engineering frontiers, hybrid energy systems, cross-sector climate impacts, and sensor/monitoring innovations.	(Hartwick et al., 2023; O’Neill et al., 2022; Kareem, 2020; Furgerot et al., 2020; Schneider et al., 2017; Du et al., 2011; Shaahid et al., 2010; Kumar et al., 2010; Pulkkinen et al., 2009)

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Figure 6.

Classification of storm impact on wind-energy systems.

Critical gaps beyond storm-type and siting classifications

Economic implications and investor cost-benefit logic of explicit storm regimes

The shift from general extreme-load envelopes to specified storm regimes has obvious economic repercussions for wind-energy investors, as presented in Table 21. Generic load envelopes give conservative design limits but can mask the true financial distribution of storm risk. They can lead to overdesign in low-risk locations or underpricing of damage, downtime, curtailment, and grid-service risk in corridors vulnerable to cyclones, typhoons, hurricanes, or windstorms. Explicit storm regimes improve investment appraisal by translating storm exposure into quantifiable economic variables, such as incremental CAPEX, protective OPEX, avoided repair cost, avoided energy-production loss, downtime reduction, improved insurance pricing, reduced forecast-error penalties and stronger debt-service confidence. This transition is crucial, especially in light of the current evidence base, which continues to be dominated by physical measurements, with energy, economic and societal indicators remaining weakly linked. Probabilistic loss estimation, firm-capacity modelling, frequency stability assessments, storm ramp analyses, and storm-event socioeconomic studies already show that storm impacts cascade from structural damage to revenue risk, market exposure, grid reliability, and investor confidence (Wilkie and Galasso, 2020a). Explicitly modelling storm regimes enables the investor to evaluate the marginal cost of resilience to the discounted value of avoided losses. The investment case is favourable when the present value of prevented repair, replacement, downtime, curtailment, imbalance, insurance and finance costs outweighs the additional cost of storm-explicit design, monitoring, forecasting and operational preparedness.

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Table 21.

Investor cost-benefit framework for shifting from extreme-load envelopes to explicit storm regimes.

Investment item	Extreme-load envelope approach	Explicit storm-regime approach	Investor benefit
CAPEX	Broad safety margins may cause overdesign or hidden underdesign	Site- and storm-specific design class	Better capital allocation
OPEX	Reactive maintenance after storms	Predictive inspection and digital monitoring	Lower unplanned maintenance
Revenue	Storm losses treated as irregular events	Energy-yield loss and curtailment explicitly modelled	Improved revenue forecasting
Insurance	Premiums based on coarse hazard assumptions	Premiums linked to quantified storm-risk profiles	Stronger insurability
Financing	Higher uncertainty in exposed corridors	Lower technical and operational uncertainty	Improved bankability
Grid penalties	Forecast errors and ramps treated operationally	Ramp-rate and reserve exposure priced earlier	Reduced imbalance risk
Asset valuation	Storm risk weakly represented in valuation	Risk-adjusted asset value estimated	More credible due diligence
Resilience investment	Difficult to justify financially	Compared using NPV, BCR, and payback	Clear investment trigger

The Net Present Value of storm-resilience investment ( N P V s t o r m ) determines if the additional costs incurred in storm-explicit design, monitoring, forecasting, protection and operational readiness are financially worthwhile throughout the lifetime of a wind-energy project. The Net Present Value of storm-resilience investment can be estimated as follows (Nwokolo et al., 2025b):

N P V s t o r m = ∑ t = 1 T ( B a v o i d e d , t − C r e s i l i e n c e , t ) ( 1 + r ) t − C 0

(1)

The decision rule is:

N P V s t o r m > 0

This means the storm-resilience investment is economically justified because the discounted avoided losses are greater than the added costs.

N P V s t o r m < 0

This means the resilience investment may not be financially attractive under the assumed costs, storm risks, and discount rate.

The storm-resilience investment benefit-cost ratio ( B C R s t o r m ) is the ratio of the benefits derived from storm-resilient measures to the expenses of implementing those measures in a wind energy project. The benefit-cost ratio can be calculated as (Obiwulu et al., 2020):

B C R s t o r m = ∑ t = 1 T ( B a v o i d e d , t ) ( 1 + r ) t C 0 + ∑ t = 1 T ( C r e s i l i e n c e , t ) ( 1 + r ) t

(2)

where C 0 is the additional upfront cost of storm-explicit design, monitoring, modelling, and protection. C r e s i l i e n c e , t is the annual cost of forecasting, inspection, maintenance, and operational readiness. B a v o i d e d , t includes avoided structural repairs, avoided downtime, avoided energy-yield losses, avoided curtailment losses, avoided imbalance penalties, reduced insurance premiums, and lower risk-adjusted financing costs.

The decision rule is:

B C R s t o r m > 1

This means the storm-resilience investment is economically justified because the discounted benefits are greater than the discounted costs.

B C R s t o r m < 1

This means the resilience investment may not be financially attractive under the assumed storm risk, cost, and revenue conditions.

Therefore, the investment is economically justified when N P V s t o r m = 0 , B C R s t o r m > 1 and the payback period is shorter than the investor’s required recovery horizon.

Conformity with, and opposition to, design standards and regulations

The classifications of multi-hazard, temporal, and system boundaries in Tables 4–20 indicate that numerous research projects either implicitly accept or challenge established design standards; nevertheless, only a few rigorously examine the sufficiency of existing load-case philosophies in light of changing storm patterns. Manwell et al. (2007) examine design parameters for offshore wind in the United States, serving as a benchmark for IEC-style load instances, whereas Castellani et al. (2015); Martín-Soldevilla et al. (2015) enhance methodologies for characterising high winds and storms across various climatic situations. Ning et al. (2025) present a KDE-based probabilistic model for offshore island wind in typhoon-prone areas, while Larsén et al. (2022) enhance design-relevant parameter spaces via the Global Atlas for Siting Parameters, both critically examining the adequacy of current design classifications in relation to basin-specific storm statistics. Kim and Manuel (2016) evaluate hurricane risk for offshore wind facilities, addressing concerns related to nacelle yaw, pitch control, and load exceedance probability during North Atlantic hurricanes. However, when these contributions are compared to the storm type and severity distribution in Tables 4–20, it is evident that formal proposals to amend or enhance design standards for floating offshore wind turbines (FOWTs) and hybrid farms in emerging storm basins remain infrequent, resulting in a significant gap between research and standards organisations.

Regulatory gap mapping against the storms-on-wind-systems architecture

A dedicated comparison with international design standards clarifies how the proposed storms-on-wind-systems architecture extends beyond conventional certification logic, as shown in Table 22. Current IEC and DNV standards provide essential foundations for structural integrity, site-condition assessment, offshore design, floating wind design, support-structure design, lightning protection, and load certification. IEC 61400-1 specifies essential design requirements for wind-turbine structural integrity and protection against hazards during the planned lifetime. IEC 61400-3-1 establishes minimum design requirements for fixed offshore wind turbines, while IEC 61400-3-2:2025 addresses external conditions and essential design requirements for floating offshore wind turbines. DNV-ST-0437 provides design requirements and guidance for determining wind-turbine loads and site conditions, and it explicitly supplements IEC 61400-1 and IEC 61400-3-1 where further guidance is needed. DNV-ST-0119 specifies general principles and requirements for floating wind turbines, while DNV-ST-0126 addresses structural design principles for wind-turbine support structures. However, these standards are still mainly organised around structural integrity, site suitability, load cases, external conditions, lightning protection, and certification requirements. The proposed architecture adds a storm-regime layer that explicitly links storm type, siting environment, turbine technology, hazard dimensionality, temporal focus, data source, scale of analysis, and system boundary. This comparison reveals regulatory gaps in compound storm regimes, basin-specific cyclone adaptation, FOWT mooring–grid coupling, wind-farm power ramps, SCADA-informed storm validation, digital-twin updating, investor risk, and grid-resilience obligations.

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Table 22.

Comparison of international wind-energy design standards with the proposed storms-on-wind-systems architecture.

Standard/framework	Main regulatory focus	Coverage in current standards	Gap exposed by storms-on-wind-systems architecture	Proposed regulatory enhancement
IEC 61400-1	General wind-turbine design requirements and structural integrity	Covers turbine subsystems, control, protection, mechanical, electrical, and support structures	Storms are treated mainly as design hazards, not as evolving storm-regime pathways affecting production, forecasting, and grid adequacy	Add storm-regime annexes linking cyclone, typhoon, thunderstorm, and windstorm classes to structural, operational, and grid metrics
IEC 61400-3-1	Fixed offshore wind-turbine design	Provides minimum design requirements for fixed offshore wind turbines	Limited treatment of clustered wind-farm behaviour, storm-driven power ramps, multi-farm interactions, and post-storm seabed degradation	Require basin-specific metocean storm catalogues and post-storm foundation-performance validation
IEC 61400-3-2:2025	Floating offshore wind-turbine design	Covers external conditions and engineering integrity of FOWTs	Does not fully operationalise compound typhoon–wave–current–mooring–grid interactions for floating arrays	Add FOWT storm-regime design cases covering mooring snap loads, shared moorings, platform drift, emergency shutdown, and grid-support impacts
IEC 61400-24	Lightning protection	Defines lightning environment, risk assessment, blade protection, structural protection, and electrical/control-system protection	Lightning is treated as a protection issue, but less connected to convective-storm sequencing, rainfall, gust fronts, and operational shutdown logic	Integrate lightning with convective-storm multi-hazard classes and storm-time operational response protocols
IEC 61400-15-1:2025	Site suitability input conditions	Provides a framework for assessing and reporting turbine/site suitability for onshore and offshore plants	Site suitability is not yet fully connected to storm-sequence memory, climate-shifted extremes, or digital monitoring feedback	Add storm-regime suitability scoring using reanalysis, LiDAR, SCADA, metocean records, and climate-adjusted storm statistics
DNV-ST-0437	Loads and site conditions	Provides requirements and guidance for load and site-condition assessment for onshore and offshore turbines	Load derivation is strong, but explicit storm-regime economics, production loss, and grid-resilience triggers remain weak	Extend load certification with storm-explicit loss, availability, ramp-rate, and curtailment metrics
DNV-ST-0119	Floating wind turbines	Defines principles and requirements for floating wind-turbine design	Floating standards remain more structural than system-integrated under extreme storms	Add cyber-physical FOWT storm-resilience requirements covering monitoring, model updating, mooring health, and post-event inspection
DNV-ST-0126	Wind-turbine support structures	Covers structural design principles for support structures	Support-structure rules are not fully coupled with changing storm climatology, soil degradation, and system downtime	Require storm-specific support-structure degradation checks and recovery-time assessment
Proposed storms-on-wind-systems architecture	Multi-dimensional storm-resilience classification	Integrates storm type, siting, technology, hazard dimensionality, temporal focus, data source, scale, and system boundary	Goes beyond single-asset certification by linking storms to structure, production, grid, finance, and policy	Use as a regulatory diagnostic layer for future IEC/DNV annexes, certification checklists, and regional storm-resilience guidelines

Equity, policy, and equitable transition aspects of storm risk to wind energy

The classifications in Tables 4–20 indicate that storm studies are primarily technical and focused on infrastructure, with less consideration of equity, policy, or just-transition aspects, despite evident disparities in storm damage across regions and communities. Shabani et al. (2025) present a four-tier framework aimed at bolstering the resilience of active distribution networks against windstorms, while Esfahani et al. (2020) examine robust resiliency-oriented operations of distribution networks during such events; however, these works emphasise performance and reliability over social vulnerability or regulatory design. Shaahid et al. (2010) underscore the off-grid electrification of remote Saudi villages through hybrid systems, implicitly addressing energy access in extreme climates, whereas Rajab et al. (2019) evaluate small urban wind turbines, and Schneider et al. (2017) investigate solid waste management in Northern Vietnam amid climate change challenges, collectively alluding to wider socio-environmental implications. Kettle (2023) chronicles the socioeconomic and energy repercussions of a particular European storm, illustrating the interplay between wind-induced outages and essential services. The significant lack of explicit connections among storm-induced wind failures, energy poverty, regulatory frameworks, finance and insurance, and coastal community resilience in Tables 4–20 highlights a substantial multidisciplinary need. Future endeavours may establish storm-resilient wind systems as a pivotal element of equitable and climate-resilient energy transitions, particularly in swiftly developing coastal areas where both wind potential and storm vulnerability are anticipated to increase.

Transfer strategy for data-sparse storm basins

Given the geographical bias in the current evidence base, a structured transfer mechanism for application of high-fidelity storm–wind models are needed for under-represented regions such as Southeast Asia, West Africa, East Africa, Southern Africa and the South Atlantic basin, as presented in Table 23. The transfer strategy development is initiated by source-model auditing, where verified models from China, the United States and Northwest Europe are classified by storm type, turbine class, foundation type, metocean force, temporal scale and system boundary. A second step should be target-region hazard mapping, using reanalysis, satellite, buoy, coastal radar, ERA5-type products, national meteorological records, and available SCADA data to define local storm regimes, including tropical cyclones, monsoon storms, squall lines, convective thunderstorms, coastal windstorms, and compound wind–rain–wave events. The third step should be physics-preserving domain adaptation, retaining transferable model components such as aeroelastic solvers, mooring-dynamics modules, fragility functions, wind-farm ramp metrics and grid-resilience indices, while local parameters such as turbulence intensity, storm translation speed, air density, wave climate, soil stiffness, bathymetry, maintenance access, grid inertia and curtailment rules are re-calibrated. The fourth phase should be hierarchical Bayesian updating, where prior distributions from data-rich locations are progressively updated by sparse local measurements from Southeast Asian or African stations, coastal buoys, mobile LiDAR, low-cost anemometer networks and wind-farm telemetry. The final phase is the surrogate-model deployment, where computationally expensive CFD, OpenFAST, OpenFOAM, and probabilistic fragility models are converted to reduced-order or machine-learning surrogates suitable for areas with inadequate computational capacity. The sixth stage is uncertainty inflation and confidence scoring, so that transferred models have wider uncertainty bounds until local validation improves. The last step should be regional demonstration and institutional embedding. Pilot studies in cyclone-prone islands, monsoon-influenced coasts, African offshore corridors and weak-grid wind zones validate transferred models against observed power ramps, cut-out events, structural responses, outage duration and restoration performance.

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Table 23.

Transfer protocol for applying high-fidelity storm–wind models to data-sparse regions.

Transfer stage	Required input	Methodological action	Expected output
Source-model audit	Validated models from China, US, Europe	Identify transferable physics and assumptions	Model-transfer inventory
Target hazard mapping	Reanalysis, satellite, metocean, station data	Classify local storm regimes	Regional storm atlas
Parameter localisation	Soil, wave, wind, turbine, grid data	Recalibrate model parameters	Localised model settings
Bayesian updating	Sparse field and SCADA data	Update priors from data-rich regions	Region-specific uncertainty bounds
Surrogate modelling	CFD, OpenFAST, fragility models	Build reduced-order emulators	Low-cost deployable tools
Pilot validation	Field measurements, outage logs, ramps	Compare predicted and observed impacts	Validation scorecard
Institutional uptake	Utilities, regulators, developers	Embed protocol in planning and operation	Transferable resilience framework

Practical implementation protocol for grid operators

The storms-on-wind-systems architecture can be formulated as a seven-step decision route that can serve as an operating protocol for grid operators. The first stage is the identification of the storm regime, as presented in Table 24. This stage uses numerical weather prediction, radar, LiDAR, satellite, buoy, SCADA and metocean data to identify the oncoming threat as a typhoon, hurricane, windstorm, thunderstorm, compound wind-wave event or generic extreme-wind episode. The second stage is asset-exposure mapping, where the storm footprint detected in the first stage is superimposed on onshore wind farms, offshore substations, floating platforms, transmission corridors, storage assets and crucial distribution nodes. Stage three is wind-farm production stress testing, estimating projected energy-yield loss, turbine cut-out likelihood, ramp-rate severity, forecast error, curtailment exposure and availability reduction. This phase is a direct development of research on offshore power ramps, high-density fleet fluctuations, neural-network-enhanced wind-power forecasting, and probabilistic wind-speed forecasts (van Den Bleeken et al., 2025). The fourth stage is grid-security assessment, where the operator translates the storm-driven wind uncertainty into reserve requirements, unit-commitment modifications, storage dispatch, demand-response activation, interconnector scheduling and frequency-stability margins. This stage is in accordance with storm condition frequency-stability analysis by Das et al. (2020), active-distribution-network resilience (Rong et al., 2023), and risk-based resilient planning and restoration techniques using offshore wind power. The fifth stage is operational alert classification, which includes Green, Yellow, Orange, and Red levels based on forecast uncertainty, ramp magnitude, reserve deficit, outage probability, and restoration complexity. The sixth level is event-time control, in which operators carry out preventive redispatch, controlled curtailment, storage assistance, reactive-power coordination, black-start preparation, and contact with wind-farm operators. The seventh stage is post-event learning, where prediction errors, curtailment volumes, outage length, ramp behaviour, reserve deployment and restoration time are captured in a digital-twin database for future model updating. This protocol operationalises the architecture from a theoretical synthesis to a practical grid-operations tool for storm planning, real-time dispatch, and post-storm resilience learning.

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Table 24.

Operator-facing protocol for implementing the storms-on-wind-systems architecture.

Stage	Operator input	Decision metric	Operational action
Storm detection	NWP, radar, LiDAR, satellite, buoy data	Storm type, lead time, intensity	Activate storm-monitoring mode
Asset exposure mapping	Wind-farm location, grid topology, substations	Exposed MW, critical nodes	Rank vulnerable assets
Production stress testing	SCADA, turbine curves, forecast ensembles	Energy loss, ramp rate, cut-out risk	Update wind-generation forecast
Grid-security assessment	Load forecast, reserves, storage, interconnectors	Reserve margin, frequency risk	Adjust unit commitment and reserves
Alert classification	Hazard and system-risk scores	Green–Yellow–Orange–Red status	Issue operator action level
Event-time control	Real-time telemetry and dispatch data	Frequency nadir, curtailment, outages	Redispatch, curtail, store, restore
Post-event learning	Event logs, SCADA, outage records	Forecast error, restoration time	Update digital twin and planning rules

Limitations, future perspectives, and conclusion

Although this study presents a pioneering, multi-dimensional mapping of storm-wind interactions across various impact classes, technologies, scales, and siting contexts, its synthesis is limited by several structural constraints. These include dependence on a finite collection of 102 peer-reviewed studies predominantly focused on Northern Hemisphere basins, utility-scale horizontal-axis turbines, and wind-only hazard representations, while inadequately addressing regions in the Global South, hybrid infrastructures, and fully coupled multi-hazard regimes such as wind-wave-current and wind-lightning-precipitation complexes. The interaction matrices indicate that existing knowledge is predominantly focused on single-turbine and component scales, with significantly fewer investigations into grid-level resilience, intersectoral connections, or just-transition ramifications for storm-affected communities. Furthermore, purely numerical or synthetic data sources continue to surpass long-duration SCADA, reanalysis, and field campaigns, thereby constraining external validity for design codes, insurance models, and operation. Future research should utilise digital twins and “digital healthcare” engineering for ageing fleets, high-resolution multi-basin climate projections, and machine-learning-enhanced probabilistic frameworks that integrate storm structure, soil-foundation-mooring behaviour, aero-servo-elastic response, and power-system stability into cohesive, open benchmark testbeds encompassing under-represented coastal tropics, rapidly urbanising hinterlands, and emerging offshore regions. The conclusion drawn from the proposed classification of storm impact categories and the array of interaction matrices encompassing siting, technology, scale, hazard dimensionality, temporal focus, data source, and system boundary is that storm resilience in wind-energy systems must evolve from a turbine-centric, case-study approach to a comprehensive, interdisciplinary science that integrates structural reliability, grid adequacy, climate-risk governance, and socioeconomic equity. The review delineates areas of substantial literature and those with minimal coverage, offering a pragmatic framework for directing observational initiatives, numerical advancements, design criteria, and policy trials, thereby establishing storm-resilient wind energy as a fundamental component of climate-resilient power systems instead of a peripheral aspect of extreme-wind engineering. For grid operators, the architecture is further translated into a stepwise operational protocol linking storm detection, wind-farm production stress testing, reserve scheduling, curtailment management, restoration planning, and post-event digital-twin learning. Economically, storm-explicit regimes convert extreme-weather resilience from an engineering contingency into an investable risk-management strategy, where added design, monitoring, and forecasting costs can be compared against avoided losses, reduced insurance exposure, improved revenue certainty, and lower financing risk. Future applications should prioritise data-sparse storm basins through transferable high-fidelity modelling, where validated tools from China, the United States, and Europe are recalibrated using local reanalysis, satellite, SCADA, metocean, and low-cost sensor data before deployment in Southeast Asian and African wind-energy corridors. Future standardisation should move from generic extreme-load compliance toward storm-regime certification, where IEC and DNV frameworks are complemented by basin-specific storm catalogues, compound hazard classes, SCADA-validated load cases, floating-array response metrics, wind-farm ramp indicators, and grid-resilience obligations.

Another drawback concerns the reproducibility boundary of the 102-study database. The review database is repeatable in terms of search strings, inclusion criteria, bibliographic metadata, thematic coding, and the development of the interaction matrix. But the key supporting evidence is not always replicable, because many studies are based on proprietary wind-farm SCADA records, confidential turbine-load measurements, confidential metocean campaigns, commercial simulation inputs, or non-public outage and restoration logs. The distinction is crucial because closed datasets can restrict independent verification of storm-load projections, power-ramp estimations, fragility curves, frequency-stability impacts, and digital-twin calibration. Thus, the term “reproducible database” should be interpreted as a reproducible categorisation and evidence mapping database, not as a fully open raw data repository. Future updates should include a data-accessibility score, require explicit reporting of code and data availability, and favour open benchmark cases based on reanalysis products, public metocean campaigns, shared storm-event catalogues, anonymised SCADA records, and standardised simulation templates. Future policy development should connect wind-energy storm resilience with insurance regulation, disaster-management planning, resilience bonds, open post-event loss registries, and community-level recovery protocols, so that storm-resilient wind systems become part of national disaster-risk-reduction infrastructure.