Search Results (399)

This study investigates the aerodynamic performance of vertical axis wind turbines (VAWTs), focusing on a novel dual-airfoil morphing mechanism for H-type Darrieus turbines. By leveraging the aerodynamic benefits of two distinct airfoil profiles, the proposed design adapts dynamically to varying wind speeds, enhancing overall efficiency. The methodology includes airfoil selection and aerodynamic analysis using the Double Multiple Stream Tube (DMST) model, simulated in QBlade software. The numerical model was validated against established benchmark data, confirming its accuracy. Key findings reveal that among all tested airfoils, the NACA 64(2)-415 airfoil achieves the highest power coefficient at low wind speeds, while the FX 84-W-127 airfoil performs optimally at higher wind speeds. Inspired by biomimetic principles, a morphing strategy and mechanism is proposed to transition seamlessly between these two profiles and enable broader operational adaptability. This innovative approach demonstrates significant potential for improving the energy capture efficiency and viability of VAWTs, contributing to the advancement of renewable wind energy technologies. Full article

Achieving sustainable energy goals requires efficient integration of renewables like wind energy. Doubly fed induction generator (DFIG)-based wind turbine systems (WTSs) operate efficiently across a range of speeds, making them well-suited for modern renewable energy systems. However, sudden wind speed variations can cause power oscillations, rotor speed fluctuations, and voltage instability. Traditional proportional–integral (PI) controllers struggle with such nonlinear, rapidly changing scenarios. A control approach utilizing support vector regression (SVR) is proposed for the DFIG wind turbine system. The SVR controller manages both active and reactive power by simultaneously controlling the rotor- and grid-side converters (RSC and GSC). Simulations under a sudden wind speed variation from 10 to 12 m per second show the SVR approach reduces settling time significantly (up to 70.3%), suppresses oscillations in rotor speed, torque, and power output, and maintains over 97% DC-link voltage stability. These improvements enhance power quality, reliability, and system performance, demonstrating the SVR controller’s superiority over conventional PI methods for variable-speed wind energy systems. Full article

Amid the global shift toward clean energy, wind power has emerged as a critical pillar of the modern energy matrix. To improve the reliability and maintainability of wind farms, this work proposes a novel hybrid fault detection approach that combines expert-driven diagnostic knowledge with data-driven modeling. The framework integrates autoencoder-based neural networks with Failure Mode and Symptoms Analysis, leveraging the strengths of both methodologies to enhance anomaly detection, feature selection, and fault localization. The methodology comprises five main stages: (i) the identification of failure modes and their observable symptoms using FMSA, (ii) the acquisition and preprocessing of SCADA monitoring data, (iii) the development of dedicated autoencoder models trained exclusively on healthy operational data, (iv) the implementation of an anomaly detection strategy based on the reconstruction error and a persistence-based rule to reduce false positives, and (v) evaluation using performance metrics. The approach adopts a fault-specific modeling strategy, in which each turbine and failure mode is associated with a customized autoencoder. The methodology was first validated using OpenFAST 3.5 simulated data with induced faults comprising normal conditions and a 1% mass imbalance fault on a blade, enabling the verification of its effectiveness under controlled conditions. Subsequently, the methodology was applied to a real-world SCADA data case study from wind turbines operated by EDP, employing historical operational data from turbines, including thermal measurements and operational variables such as wind speed and generated power. The proposed system achieved 99% classification accuracy on simulated data detect anomalies up to 60 days before reported failures in real operational conditions, successfully identifying degradations in components such as the transformer, gearbox, generator, and hydraulic group. The integration of FMSA improves feature selection and fault localization, enhancing both the interpretability and precision of the detection system. This hybrid approach demonstrates the potential to support predictive maintenance in complex industrial environments. Full article

Due to the structural aspects of the wind turbine, such as wind shear and tower shadow effects, the output power of the wind turbine has periodic fluctuations, known as 3P fluctuations. These fluctuations can reduce overall power generation and deteriorate power quality. In this context, this paper proposes a power smoothing control method that utilizes rotor inertia and a DC-link capacitor as small-scale energy storage devices. First, the typical energy storage capacities of the rotor’s rotational kinetic energy and the DC-link capacitor’s electrostatic energy are analyzed to assess their smoothing potential. Secondly, a control method is presented to apply the rotor and the DC-link capacitor as small-scale energy storage, with the smoothing frequency range allocated according to their respective storage capacities. Finally, the proposed method is compared with the conventional maximum power point tracking (MPPT) method and the 3P-notch filter method. The effectiveness of the proposed algorithm is verified through MATLAB/Simulink simulations, demonstrating its capability to mitigate periodic power fluctuations. The results showed that the proposed control method is applicable, reliable, and effective in mitigating periodic power fluctuations. Full article

This paper focuses on the influence of torsional loading on the vibration-based unbalance fault diagnosis technology under variable-speed conditions. The coupled flexural–torsional nonstationary governing equations of motion are obtained and solved numerically. Taking the short-time chirp Fourier transform from the acceleration signal, which is determined from the numerical solutions, the influence of variable loading on the magnitude of the fundamental rotational harmonic—a diagnostic feature for conventional unbalance diagnosis technology—as well as its speed-invariant version for novel unbalance diagnosis technology is assessed. Numerical assessment shows that despite the stationary conditions, where the first rotational harmonic magnitude is independent from the torsional load, the conventional unbalance technology depends on the variable torsional load. However, the novel speed-invariant diagnostic technology is independent of the variable torsional load. The dependency of the conventional unbalance fault diagnosis technology on the variable torsional load and the independency of the novel speed-invariant unbalance diagnostic technology on the variable loading are justified by performing thorough experimental investigations on a variable-speed wind turbine with a permissible level of unbalance. Full article

This study investigates the performance of medium-power wind turbines (within kilowatt range) in response to substantial fluctuations in wind speed. The wind turbines utilize induction generators that have a short-circuited rotor and are controlled by a power converter within the stator circuit. This configuration facilitates the adjustment of the stator frequency, thereby allowing the desired rotational speed to be achieved and guaranteeing that the turbine operates at the maximum power point (MPP). Specific mathematical models for the turbine and generator have been developed using technical data from an operational wind turbine. The study demonstrated that utilizing a power converter within the stator circuit enhances the turbine’s operation at its maximum power point. A crucial aspect of effective MPP operation is the accurate determination of the relationship between wind speed and the corresponding optimal angular mechanical speed. Precise understanding and implementation of the interdependence among the primary generator parameters—namely power, frequency, current, and power factor—in relation to wind speed is essential for maximizing power generation and achieving grid stability for wind turbines operating in variable wind speed. Full article

Single-phase short-circuit faults are severe asymmetrical fault modes in high renewable energy power systems. They can easily cause large-scale renewable energy to enter the low-voltage ride-through (LVRT) state. When such symmetrical or asymmetrical faults occur in the transmission channels of high-proportion wind power clusters, they may trigger the tripping of thermal power units and a transient voltage drop in most wind turbines in the high-proportion wind power area. This causes an instantaneous active power deficiency and poses a low-frequency oscillation risk. To address the deficiencies of wind turbine units in fault ride-through (FRT) and active frequency regulation capabilities, a power emergency support scheme for wind power clusters based on doubly fed variable-speed pumped storage dynamic excitation is proposed. A dual-channel energy control model for variable-speed pumped storage units is established via AC excitation control. This model provides inertia support and FRT energy simultaneously through AC excitation control of variable-speed pumped storage units. Considering the transient stability of the power network in the wind power cluster transmission system, this scheme prioritizes offering dynamic reactive power to support voltage recovery and suppresses power oscillations caused by power deficiency during LVRT. The electromagnetic torque completed the power regulation within 0.4 s. Finally, the effectiveness of the proposed strategy is verified through modeling and analysis based on the actual power network of a certain region in Northeast China. Full article

The structural integrity of next-generation offshore wind turbines is highly sensitive to inflow variability, yet current standards often simplify wind conditions without capturing their combined effects on dynamic loads. To address this, we analyzed the NREL IEA 15 MW offshore wind turbine using 27 simulation cases strategically selected through Latin Hypercube Sampling (LHS) from a design space of over 14 million combinations. Four key environmental variables—Extreme Wind Speed (30–40 m/s), turbulence intensity (12–16%), Shear Exponent (0.1–0.3), and Flow Inclination Angle (−8° to +8°)—were varied to assess their influence on structural response using BLADED simulations. Results showed that the combined structural moment (Mxyz) ranged from 159,502.5 kNm (minimum) to 189,829.2 kNm (maximum), indicating a 19% increase due to inflow conditions. Maximum-moment case exhibited a 2.6× higher drag coefficient, a 13% rise in pitch bearing moment, and dominant frequency content near 0.175 Hz, closely matching the first tower side-side natural mode (0.17593 Hz), confirming potential resonance. These findings highlight the importance of multidimensional inflow modeling for identifying worst-case load scenarios and establishing a foundation for future load prediction models and support structure optimization. Full article

With the increasing penetration of renewable energy sources, reduced system inertia and weakened frequency regulation capability have emerged as critical issues in power systems. As a result, wind turbines are now required to provide frequency support functions. To enable accurate analysis of the operational characteristics of wind turbines equipped with such control functions, this study proposes a virtual wind turbine model that estimates the operating point of a wind turbine in real-time under the assumption that frequency support functions are not performed. The proposed model is based on a turbine state observer that estimates wind speed and the power coefficient, and subsequently estimates generator power, generator speed, and blade pitch angle across various operating modes. Simulations were conducted under conditions with fluctuating wind speed and grid frequency, including MPPT, speed control, and pitch control operating regions. The accuracy of the proposed estimation model was evaluated, and the results demonstrated low estimation errors for key variables such as generator speed, power output, pitch angle, and wind speed across all conditions. These results quantitatively validate the robustness and applicability of the proposed model. Full article

Variable Speed AC-excited Pumped Storage Units (VSACPSUs) demonstrate advantages in flexibility, high efficiency, and fast response, and they play a crucial regulatory role in power systems with increasing renewable energy penetration. Typically connected to weak grids, conventional low-voltage ride-through (LVRT) control methods for these units suffer from single control objectives, poor adaptability, and neglect of electromechanical coupling characteristics. To address these limitations, this paper proposes a multi-objective optimization strategy considering electromechanical coupling under a grid voltage fault. Firstly, a positive/negative-sequence mathematical model of doubly-fed machines is established. Based on stator winding power expressions, the operational characteristics under a grid fault are analyzed, including stator current imbalance as well as oscillation mechanisms of active power, reactive power, and electromagnetic torque. Considering the differences in rotor current references under different control objectives, a unified rotor current reference expression is constructed by introducing a time-varying weighting factor according to expression characteristics and electromechanical coupling properties. The weighting factor can be dynamically adjusted based on operating conditions and grid requirements using turbine input power, grid current unbalance, and voltage dip depth as key indicators to achieve adaptive control optimization. Finally, a multi-objective optimization model incorporating coupling characteristics and operational requirements is developed. Compared with conventional methods, the proposed strategy demonstrates enhanced adaptability and significantly improved low-voltage ride-through performance. Simulation results verify its effectiveness. Full article

As the global transition to sustainable energy accelerates, wind power remains pivotal in reducing carbon emissions and achieving renewable energy targets. However, greater reliance on wind energy systems increases susceptibility to cyberattacks, notably False Data Injection (FDI) attacks, which manipulate operational data and undermine the decision-making critical for efficient energy production. This study introduces a novel analytical framework to assess the impact of FDI attacks on variable-speed wind turbine output power. Simulations, conducted using a MATLAB-based induction generator model, evaluate the effects of injecting false data into parameters such as wind speed, blade pitch angle, and generator angular speed. Results demonstrate that FDI attacks targeting wind speed induce significant power output deviations, causing decision-making errors that threaten operational reliability. In contrast, pitch angle manipulations have negligible effects on power generation. These findings emphasize the urgent need for robust cybersecurity measures to protect wind energy infrastructure from evolving cyber-threats. This research advocates advanced detection and mitigation strategies to enhance system resilience, ensuring wind power’s role in a low-carbon future. By identifying critical vulnerabilities, the analysis informs policymakers and industry stakeholders, guiding investments in cybersecurity to safeguard renewable energy systems. Such efforts are essential to maintain operational stability and support global sustainability goals, reinforcing wind power’s contribution to clean energy transitions. Full article

To modernize electrical power systems on isolated islands, countries around the world have increased their interest in combining green energy with conventional power plants. Wind energy (WE) is the most adopted renewable energy source due to its technical readiness, competitive cost, and environmentally friendly characteristics. Despite this, a high penetration of WE in conventional power systems could affect their stability. Moreover, these isolated island power systems face frequency deviation issues when operating in hybrid generation mode. Generally, under contingency or transient conditions for hybrid isolated wind–diesel power systems (WDPSs), it is only the diesel generator that provides inertial support in frequency regulation (FR) because wind turbines are unable to provide inertia themselves. Frequency deviations can exceed the pre-defined grid code limits during severe windy conditions because the diesel generator’s inertial support is not always sufficient. To overcome this issue, we propose a control strategy named emulation inertial and proportional (EI&P) control for Variable-Speed Wind Turbines (VSWTs). VSWTs can also contribute to FR by releasing synthetic inertia during uncertainties. In addition, to enhance the effectiveness and smoothness of the blade pitch angle control of WTs, a pitch compensation (PC) control loop is proposed in this paper. The aim of this study was to provide optimal primary frequency regulations to hybrid wind–diesel power systems (WDPSs). Therefore, the hybrid WDPS on San Cristobal Island was considered in this study. To achieve such goals, we used the above-mentioned proposed controls (EI&P and PC) and optimally tuned them using the Student-Psychology-Based Algorithm (SPBA). The effectiveness of this algorithm is in its ability to provide the best optimum controller gain combinations of the proposed control loops. As a result, the FD in the WDPS on San Cristobal Island was reduced by 1.05 Hz, and other quality indices, such as the integral absolute error (IAE), integral square error (ISE), and controller quality index (Z), were improved by 159.65, 16.75, and 83.80%, respectively. Moreover, the proposed PC control, which was further simplified using exhaustive searches, resulted in a reduction in blade pitch angle control complexity. To validate the results, the proposed approach was tested under different sets of perturbations (sudden loss of wind generator and gradual increase in wind speed and their random behavior). Furthermore, hybrid systems were tested simultaneously under different real-world scenarios, like various sets of load or power imbalances, wind variations, and their combinations. The Simulink results showed a significant improvement in FR support by minimizing frequency deviations during transients. Full article

The Raglan mining site in northern Quebec relies on diesel for electricity and heat generation, resulting in annual emissions of 105,500 tons of CO₂ equivalent. This study investigates the feasibility of decarbonizing the site’s power generation system by integrating a renewable energy network of wind turbines and a pumped hydro storage plant (PHSP). It uniquely integrates PHSP modeling with a dynamic analysis of variable wind speeds and extreme climatic conditions, providing a novel perspective on the feasibility of renewable energy systems in remote northern regions. MATLAB R2024b-based simulations assessed the hybrid system’s technical and economic performance. The proposed system, incorporating a wind farm and PHSP, reduces greenhouse gas (GHG) emissions by 50%, avoiding 68,500 tons of CO₂ equivalent annually, and lowers diesel consumption significantly. The total investment costs are estimated at 2080 CAD/kW for the wind farm and 3720 CAD/kW for the PHSP, with 17.3 CAD/MWh and 72.5 CAD/kW-year operational costs, respectively. The study demonstrates a renewable energy share of 52.2% in the energy mix, with a payback period of approximately 11 years and substantial long-term cost savings. These findings highlight the potential of hybrid renewable energy systems to decarbonize remote, off-grid industrial operations and provide a scalable framework for similar projects globally. Full article

Power systems are currently undergoing a transition from centralized synchronous generators to decentralized non-synchronous generators that rely on renewable energy sources. This shift poses a challenge to system operators, as the high penetration levels of renewable energy introduce variability and changes in the physics of power systems. Load-frequency control is one of the biggest challenges faced by electrical grids, especially with increased wind energy penetration in recent years. The inertial controller is one of the methods used to support system frequency in variable-speed wind turbines. In this study, a proportional resonant (PR) controller was added to an inertial controller to achieve better frequency regulation by controlling the active power of the doubly fed induction generator (DFIG). First, the impact of the PR controller parameters on the frequency deviation, overshoot, settling time, and system stability was investigated to identify the optimal values that achieved the lowest frequency deviation while maintaining system stability. Second, the performance of the proposed method was compared that of the traditional method under different load perturbations. The results prove that improperly determining the proportional gain of the PR controller negatively affects system stability and frequency deviation. In addition, the results validate the hypothesis that the proposed method would provide fast frequency support for all the studied cases. The analysis and simulation of these scenarios were performed using the MATLAB/SIMULINK program. Full article

Optimizing wind turbine control is a major challenge due to wind variability and nonlinearity. This research seeks to improve the performance of wind turbines by designing and developing hybrid intelligent controllers that combine advanced artificial intelligence techniques. A control system combining deep neural networks and fuzzy logic was implemented to optimize the efficiency and operational stability of a 3.5 MW wind turbine. This study analyzed several deep learning models (LSTM, GRU, CNN, ANN, and transformers) to predict the generated power, using data from the SCADA system. The structure of the hybrid controller includes a fuzzy inference system with 28 rules based on linguistic variables that consider power, wind speed, and wind direction. Experiments showed that the hybrid-GRU controller achieved the best balance between predictive performance and computational efficiency, with an R² of 0.96 and 12,119.54 predictions per second. The GRU excels in overall optimization. This study confirms intelligent hybrid controllers’ effectiveness in improving wind turbines’ performance under various operating conditions, contributing significantly to the field of wind energy. Full article