Wind Engineering

The integration of renewable energy into energy systems is advancing, yet the inherent uncertainty of wind and solar generation poses challenges to maintaining supply-demand balance. Existing research often optimizes individual energy sources or loads in isolation, lacking a holistic methodology to address supply-demand uncertainties and incorporate intraday response mechanisms. This paper proposes an intraday optimization approach that integrates carbon emissions, storage costs, unit costs, and interconnection fluctuations into a robust model. It ensures a power-heat equilibrium and establishes a source-load uncertainty framework. Real-time adjustments of loads, storage, and diesel generation are achieved through rolling optimization. A genetic algorithm optimizes storage output, while a dual-side unbalanced power model enables bilateral balancing control. The results demonstrate a source-load deviation of 0.2%, a wind-solar fluctuation of 1% per minute, a 1.59% increase in solar utilization, and a reduction in emissions by 0.75 t.

Due to the depletion of fossil fuel resources and their environmental impact, the global energy market is transitioning toward renewable technology, with wind turbines playing an important role. However, due to their unpredictable nature, the functioning of the grid is not reliable. This paper proposes a new control technique for wind-based connected energy conversion systems using an online multi-objective PSO algorithm, which enhances the robustness and stability of the system with greater efficiency in maximum power point tracking (MPPT). It also optimizes dynamic performance by fine-tuning the proportional integral (PI) regulator parameters. In addition, a machine learning (ML)-based fault detection framework has been implemented to improve reliability by recognizing and diagnosing turbine defects. The simulation findings show that integration of the multi-objective PSO algorithm in the PI controller ensures optimal system control, whereas ML-driven diagnostic tools successfully categorize defects and contribute to improved operational performance.

Early detection of inter-turn short-circuit faults in the stator windings of Doubly-Fed Induction Generators (DFIGs) is essential to ensure proper maintenance planning and avoid critical failures. This paper presents a technique to detect and quantify this type of fault to assess its severity. The proposed approach consists of analyzing the rotor currents of DFIGs in Park reference frame. A theoretical study is carried out, accompanied by experimental validation using a test bench and a data acquisition (DAQ) system. This study reveals a new fault indicator based on analysis of geometric shape of rotor currents. Quantifying the evolution of this indicator enables the estimation of defect severity with a relative error of 2%. Qualitative and quantitative analysis of it facilitates the maintenance supervisor’s decision-making process. The results demonstrate the potential of this approach, which is characterized by its simplicity, reliability and effectiveness.

Wind energy development requires accurate resource assessment at increasing turbine hub heights. Tall meteorological masts are costly and logistically complex, making remote sensing technologies like SoDAR a practical alternative—provided they are rigorously validated. This study validates a Vaisala Triton SoDAR against a 120 m met mast over 1 year at the Kayathar site in India. Correlation analysis of 10-minute records at 60 m, 90 m, and 120 m revealed strong agreement in wind speed (R² > 0.95) and direction (R² > 0.99), with minor deviations at greater heights. A comparison of Power Law and Logarithmic Law extrapolation methods showed comparable results below 60 m, but notable divergence above that level, highlighting the need for height-dependent wind shear coefficients. The findings offer practical guidance for wind measurement and prediction, with direct relevance to modern tall wind turbine deployments.

To compensate for the massive variations in power output brought on by unpredictability, enormous quantities of pricy battery storage or power reserve capacity are required. Precise prediction of the Solar Wind Power Forecasting system improves energy conversion efficiency, reduces the risk of overloading the system, and optimizes unit commitment. In this paper, design a SWIFT-Net (Solar & Wind Integrated Forecasting Technology Network) model for precise solar and wind energy forecasting. Unlike existing models, SWIFT-Net uniquely integrates a deep learning ensemble framework with a dual-stage hybrid feature engineering mechanism using Hybrid Kookaburra Optimization and Botox Optimization, which has not been previously applied in this context. This ensures the selection of the most impactful features and enhances generalization across variable weather conditions. The weighted averaging method is employed to combine solar and wind power predictions. Finally, the designed model gained results are validated with existing classifiers in terms of MSE, RMSE, and MAE, ensuring continual refinement for enhanced forecasting accuracy, reliability.

This study presents a three-dimensional computational fluid dynamics (CFD) investigation of a flanged conical diffuser, focusing on the aerodynamic benefits of integrating an inlet nose geometry. Simulations were conducted in Ansys 2019 R3 using the SST k–ω turbulence model with non-linear eddy viscosity corrections to capture flow separation and reattachment. Validation against experimental data showed excellent agreement for the baseline case. The nosed configuration yielded marked improvements: higher peak velocity ratios, increased maximum pressure coefficients, and enhanced overall pressure recovery. Flow-field analysis revealed that the inlet nose effectively mitigates adverse pressure gradients, delays boundary layer separation, and weakens recirculation zones. Turbulence kinetic energy was significantly reduced near walls and in the core region, indicating lower energy dissipation. Overall, the results demonstrate that the nose geometry functions as an efficient passive flow control device, offering substantial aerodynamic gains for diffuser-augmented wind turbines and other renewable energy systems.

Small-scale H-Darrieus turbine (HDT) often struggles to reach self-sustained power producing speed due to low start-up torque. Low torque is sometimes manifested in the form of torque plateau or ‘dead band’. To improve torque development, turbine design variables such as blade chord-to-radius ratio (c/r) and solidity (σ) play critical role. To understand the effect of c/r only, this study analyses turbines with identical σ but different c/r using unsteady computational fluid dynamics (CFD). Results show that larger c/r enhances torque by influencing the leading-edge flow and vortex formation and convection. Two mechanisms are identified: (i) amplification of the variation of incidence angle along blade chord, effectively acting as aerofoil-camber morphing, and (ii) high blade pitch rate effect, especially at low λ (<1) where intra-cycle pitching is significant. These findings highlight the sensitivity of torque generation to c/r, offering design guidance for improving HDT start-up performance.

The growing penetration of wind energy in global power systems necessitates advanced testing platforms for control development, component validation and grid-integration studies. Wind turbine emulators (WTEs) offer a viable solution, replicating turbine behavior under controllable laboratory conditions. This work presents the development and experimental validation of a WTE that uses a three-phase induction motor controlled by a hybrid Direct Torque Control with Space Vector Modulation (DTC-SVM) technique. The aerodynamic and mechanical dynamics of a real wind turbine are mathematically modeled, scaled, and integrated with the induction motor’s electromechanical model with inertia difference taken into consideration. To validate the proposed WTE, simulation studies and hardware implementation were carried out. A complete experimental test-bench was built in the laboratory using an 800 W motor, an IGBT inverter and a PC interfaced to a dSPACE DS1104 board. Simulation and experimental results confirm that the proposed WTE is capable of reproducing the torque-speed and power-speed curves across various operating conditions, including wind speed and load changes, with mean average error not exceeding 3%. The DTC-SVM control offers fast dynamic response with minimized torque ripples of about 10% only.

The growing demand for sustainable energy has driven research into innovative wind energy harvesting technologies. Vortex bladeless wind turbines (BWTs) present a promising alternative to conventional turbines, offering reduced mechanical complexity, improved reliability, and suitability for micro-grid and urban applications. Recent studies have explored resonance-tuned masts, linear generators, and piezoelectric transducers, but challenges remain in achieving high voltage output, wide operational range, and experimental validation of grid-connected systems. This paper presents a bladeless wind turbine prototype integrating a resonance-tuned mast with a linear generator and DC-DC boost converter, designed to maximize energy extraction under varying wind conditions. The system is evaluated experimentally under both on-grid and off-grid conditions, and results are corroborated with COMSOL Multiphysics and MATLAB simulations. The proposed design demonstrates enhanced energy conversion efficiency, improved voltage output, and practical scalability, addressing key limitations of previous BWT studies and advancing the field toward urban and micro-grid deployment. Experimental findings demonstrate agreement with theoretical predictions.

The increased demand for clean renewable energy requires innovative technology designs. A dual-rotor wind turbine system is presented and modelled using Blade Element Momentum Theory. Four different rotor configurations were analysed in both structural loading and vibrational impact. A key advantage of the dual-rotor approach is its ability to maintain partial operation even if one rotor fails, a feature that could significantly increase wind farm reliability. The study explores contra-rotation as a strategy to mitigate asymmetric lateral loads, thereby reducing torsional and rolling stresses at the tower and beam locations – a critical factor for structural longevity. This investigation analysed the impact of rotor phasing, an area previously unaddressed in the literature, by focussing on system dynamics in both frequency and time domains. Our findings reveal that operating the rotors out of phase can reduce fore-aft vibrations in the connecting beam by up to 86%, without increasing overall structural loads.

To continuously generate micro-power at low-wind speeds without human intervention, this study proposes combining an electromagnetic energy harvester with an Archimedes vertical axis wind turbine. The system includes a linear electromagnetic component featuring a freely moving magnet within a tube, around which a coil is wrapped. This setup is driven by a horizontal shaft connected to an Archimedes spiral turbine. Two different methods were implemented to enhance the movement of the magnet within the coil and counteract the centrifugal force generated by the turbine’s rotation. The first method involved placing two magnets with opposite poles at each end of the tube, creating a magnetic repulsion force to push the magnet back toward the coil. In the second approach, two springs were attached to the magnet. Their stiffness generated a backward force, guiding the magnet along the tube toward the coil. The relationship between wind speed and generated voltage was derived mathematically and verified through experimental measurements. For Type 1 and Type 2 harvesters, the maximum generated voltages, 4.1 mV and 4.84 mV, respectively, were recorded at wind speeds of 3.15 m/s and 3.51 m/s. The experimental results show that the spring mechanism performs more efficiently than the opposite-pole magnetic configuration in generating the backward force required to counteract the centrifugal force acting on the free-moving magnet. At low-wind speeds, the experimental findings show reasonable agreement with the theoretical predictions. Incorporating a spring-loaded mechanism to enhance the backward force on the magnet enables the system to counteract centrifugal forces more efficiently than magnetic repulsion alone, resulting in improved voltage generation. The integration of the Archimedes spiral turbine with a linear electromagnetic harvester represents an innovative engineering approach. This configuration enables continuous and predictable power output, even under low and irregular wind-speed conditions, potentially establishing a foundation for similar energy harvesting designs. Owing to its modular structure and low production cost, the proposed harvester can be scaled for larger installations or tailored to specific applications, such as sensors, IoT devices, and other low-power electronic systems, thereby contributing to the advancement of green energy technologies.

With the increasing scale of vertical-axis wind turbines (VAWTs), the aeroelastic behavior of long and flexible blades poses significant challenges to their structural dynamics. To investigate these effects, a two-way fluid–structure coupling approach was employed using a single VAWT blade under standstill conditions (0° angle of attack). Simulations across various Reynolds numbers (Re) revealed that the dominant vibration modes were flapwise and edgewise, concentrated in low-order modes, while torsional vibration appeared only in higher-order modes. Periodic oscillations of lift and drag were driven by structural vibrations. As Re increased, the blade’s dynamic response evolved from a single mode to multiple coupled modes, with new frequency components aligned with the inflow direction showing characteristics of first-order flapwise motion. At high Re, intensified aerodynamic load fluctuations induced multimodal coupling and more complex responses, highlighting pronounced aeroelastic instability.

This paper provides a comparative analysis between two turbine structures that differ only in the use of either a structure of permanent magnets generator with sinusoidal waveforms or its equivalent structure of permanent magnets generator with trapezoidal waveform. The design parameters of turbines using these two structures of generator are optimized by a developed Genetic Algorithm, in the goal to maximize the recovered power. Indeed, the comparison between the two wind turbines leads to the choice of the turbine using a trapezoidal waveform generator enabling an approximately 25% increase in recovered energy. This result represents the principal finding of this study. In addition, this study is completed by the analysis of the energy performance of the chosen wind turbine by the use of a booster chopper for recovered energy optimization. A braking system regulating the generator speed is added to these turbine structures. The braking system plays an important role in the protection of the turbine against over-currents and subsequently against the burning of the turbine considering, that the magnitude of the back electromotive forces increases depending on the elevation of the angular speed of the turbine. The overall study is validated by implementation of the studied models under SIMULINK simulation environment.

Among the various alternatives to traditional generators, the brushless doubly fed induction machine (BDFIM) is particularly well suited for variable-speed applications. However, advanced control strategies are required to fully exploit its capabilities. This study therefore proposes a novel cascaded field-oriented control (FOC) strategy incorporating complex fuzzy logic controllers (CFLCs). The primary objective of this approach is to introduce complex-valued control while reducing the overall dimensionality of the control scheme by exploiting both the magnitude and phase components of the complex control parameters. To assess the effectiveness of the proposed CFLC structure, simulations were carried out in the MATLAB/SIMULINK environment on a BDFIM system combined with a Maximum Power Point Tracking (MPPT) algorithm for wind energy generation. The results demonstrate clear and significant advantages over a standard Complex Proportional Integral (CPI) controller. The CFLC achieves a more efficient trajectory in the complex plane, reflecting smoother and more direct transitions. Additionally, it exhibits reduced power coupling and overshoot, an accelerated phase response, and enhanced voltage regulation, which may mitigate equipment stress. These findings underscore the effectiveness of the CFLC and highlight promising alternatives, thereby providing a strong impetus for continued research in the emerging domain of complex fuzzy control.

Short-term wind power forecasting supports grid stability despite wind’s intermittency, but deep learning (DL) models often require excessive computation, hindering real-time deployment. This study compares three classical regression models, linear regression (LR), k-nearest neighbors (KNNs), and support vector regression (SVR) using 10-minute SCADA data. The data retained raw variability, with only negative power values set to zero. Hyperparameters of KNN and SVR were optimized using GridSearchCV with time series cross-validation. Optimized SVR achieved superior performance (MAE = 183.93 kW, RMSE = 387.64 kW, R² = 0.91, MAPE = 144.88%, MdAE = 84.44 kW), outperforming KNN (R² = 0.79) and LR (R² = 0.91 but higher errors). Scatter plots and residuals highlighted SVR’s ability to handle nonlinearities. Results suggest that SVR is a viable candidate SVR for low-resource, real-time forecasting in renewables.

Research on bladeless wind turbines (BWTs) based on vortex-induced vibration has increased due to the need for sustainable energy and the limitations of conventional wind turbines. BWTs eliminate complex rotating components such as blades and gearboxes, offering a simpler and potentially more reliable design. This study presents a cost-effective numerical and experimental investigation of BWT performance using conic and double frustum masts mounted on a flexible beam. The MATLAB Simulink was used to anlayze the vibration dynamics of the BWT mast, while experimental tests were conducted in a subsonic wind tunnel to analyze the single and double frustum mast. The mast vibrations were recorded using a 60 fps camera, and tip displacement was obtained through an open-source video-based analysis method. The study evaluates vibration frequency, amplitude, and displacement to improve energy harvesting efficiency. Results indicate that the double frustum mast achieves a maximum tip displacement of 0.021 m and exhibits more stable oscillations than the conic mast at a lock-in velocity of 1.7 m/s.

Prolonged operation of wind turbines leads to blade contamination and erosion, increasing surface roughness and significantly degrading aerodynamic performance. This study employs two-dimensional steady Reynolds-Averaged Navier-Stokes (RANS) simulations to evaluate the impact of surface roughness on DU, FFA, and NACA airfoils across various thicknesses. Traditional roughness models often oversimplify geometries, so a roughness strip is introduced to simulate realistic erosion patterns on the airfoil surfaces. Coupled with the γ-Re_θ transition SST model, the present simulations effectively capture boundary-layer transitions and flow separation, yielding more accurate results. The results indicate that roughness impacts are highly dependent on the airfoil type and thickness. For the same thickness, blunt trailing-edge airfoils exhibit greater resistance to roughness effects; the NACA-63430 airfoil experiences a 35% reduction in maximum lift and a forward shift of 4° in stall angle due to surface roughness. Conversely, the DU97-W-300 airfoil only shows a 27% decrease in maximum lift and a stall angle shift forward of 2°. In general, thin airfoils exhibit greater flow stability and less sensitivity to roughness compared to their thick counterparts. Furthermore, as the Reynolds number increases, the negative effects of surface roughness on the airfoil are diminished.

Offshore wind turbine (OWT) selection is crucial for the economic viability and stability of offshore wind power projects. However, it is complicated by expert linguistic ambiguity and conflicting criteria, which introduce uncertainties. This paper proposes a novel multi-criteria decision-making (MCDM) system combining principal component analysis (PCA) and cubic bipolar fuzzy extension of improved ELECTRE-I (CBF-ELECTRE-I+). First, twelve secondary indicators are identified through literature review, and their weights are determined by PCA to reduce expert subjectivity. Next, expert evaluations of alternatives are quantified as CBF sets and then aggregated using the weighted geometric aggregation operator, thereby capturing incomplete expert information and integrating divergent opinions. Finally, the optimal scheme is determined by computing the significance of each OWT via CBF-ELECTRE-I+. Compared with the traditional CBF-ELECTRE-I, the proposed CBF-ELECTRE-I+ enhances ranking accuracy and simplifies the computational process. A case study demonstrates the effectiveness of the proposed system for OWT selection.

The increasing penetration of wind energy and the growing vulnerability of communication channels pose major challenges to maintaining reliable power system operation. This paper presents a hybrid Grey Wolf Optimizer–Cuckoo Search (GWO–CS) tuned Model Predictive Controller (MPC) for resilient simultaneous frequency and voltage regulation in a two-area interconnected system dominated by wind, supported by thermal and diesel units. The MPC cost-function weights are optimally tuned using a normalized performance index to achieve coordinated control of the Automatic Voltage Regulator (AVR) and Load Frequency Control (LFC) loops. The proposed GWO–CS–MPC demonstrates superior performance over PID, FOPID, and other metaheuristic controllers, achieving faster settling, minimal overshoot, and strong disturbance rejection. Under nonlinearities, parameter uncertainties, and Denial-of-Service (DoS) attacks causing communication delays, the controller maintains stability and dynamic performance. Results confirm that the proposed method ensures robust, cyber-resilient operation of wind-integrated smart grids.

This paper describes an appropriate hybrid control mechanism to enhance the turbine peak output torque of a DFIG-based grid-connected wind energy conversion system at variable wind speed conditions. These sudden variations pose a critical challenge to maximizing output torque and other associated optimal parameters. To address the identified research gaps, this study aimed to design a novel hybrid ANFIS-PI controller for a back-to-back converter, confirming superior efficiency and responsiveness. The recent MATLAB–Simulink model simulation results illustrate that an intended ANFIS-PI controller achieved a peak output torque of 13.31 kNm, compared with 13.25 kNm for the FLC-PI controller, both evaluated against a PI-controller benchmark of 12.09 kNm. To achieve these results by enhancing the maximum power coefficient to 0.55 by the ANFIS-PI, compared with 0.547 from the FLC-PI and 0.4801 for the PI controller, at an optimal blade angle of 0° and the maximum speed of 12.5 m/s.