Accurate yaw alignment is critical for maximizing power capture in horizontal-axis wind turbines, as even moderate yaw misalignment leads to significant aerodynamic losses, increased actuator usage, and accelerated mechanical wear. This research paper proposes a hybrid smart yaw control system for small-scale wind turbines that combines real-time measurements with short-term wind direction prediction to improve alignment accuracy, operational reliability, and energy efficiency under realistic operating conditions. The system integrates four wind direction information sources, such as physical wind vane sensing, live online weather data, forecast data, and a data-driven prediction module within a structured priority framework (VANE → LIVE → FORECAST → AI), to ensure continuous yaw control during sensor or communication unavailability. The prediction module is based on a long short-term memory (LSTM) neural network trained in MATLAB using live data from an online platform, with sine–cosine encoding employed to address the circular nature of directional data. The yaw controller incorporates a ±15° deadband, dwell-time logic, shortest-path rotation, and cable-safe constraints to reduce unnecessary actuation while maintaining effective alignment. The proposed system is validated through MATLAB/Simulink simulations and real-time microcontroller-based experiments using a stepper motor-driven nacelle. Compared with conventional vane-based yaw control, the hybrid AI-assisted approach reduces the average yaw error by approximately 35–45%, maintains a yaw error within ±15° for more than 90% of the operating time, increases average electrical power output by 3–5%, and reduces yaw motor energy consumption by 10–15%, while decreasing corrective yaw actuation events by 30–40%. These results demonstrate that integrating an LSTM-based wind direction predictor with multi-source wind data provides a robust, low-cost, and practically deployable yaw control solution that enhances energy capture and mechanical durability in small-scale wind turbines.

This paper presents the potential causes of component failures in wind turbine systems that affect their reliable and efficient operation. Component failures in wind turbines can lead to complete system failure, resulting in downtime, reduced reliability, and increased costs. To fully utilize wind energy, minimizing the risk of component failures is essential. By applying the FMECA method (Failure Mode Effects and Criticality Analysis – FMECA), critical components of wind turbine systems have been identified, providing the opportunity to prioritize problem-solving. The results emphasize the importance of maintenance and design optimization to reduce the risk of failures and maximize the utilization of wind energy. Introduction/purpose: The reliability of wind turbine systems plays a crucial role in ensuring a stable and efficient electricity supply from renewable sources. The failure of any component can lead to system downtime, reduced reliability, and increased operational costs. In this context, it is essential to identify and analyze potential failures in order to improve overall system reliability. The aim of this paper is to analyze the reliability of a wind turbine system using the FMECA method. The focus is on identifying the most critical components and understanding the causes and consequences of their failures, thereby contributing to the improvement of system design, maintenance, and operation. Methods: This paper applies the FMECA (Failure Modes, Effects and Criticality Analysis) method, which enables a detailed assessment of potential system failures, their causes and effects, and the identification of the most critical system points based on quantitative parameters. The methodology includes the following steps: identification of key components of the wind turbine, including the rotor, gearbox, generator, control system, and other subsystems; definition of possible failure modes for each component, with corresponding mechanisms that may lead to failure (e.g., wear, overheating, mechanical damage, etc.); evaluation of the consequences of failures, both on the specific component and on the overall operation of the wind turbine; quantitative risk assessment through the assignment of values for: the probability of potential failure occurrence (R1), the severity of the potential failure (R2), and the probability of detecting the failure and preventing its manifestation (R3); calculation of the criticality level (R) using the expression: 𝑅𝑅 = 𝑅𝑅1 ∙ 𝑅𝑅2 ∙ 𝑅𝑅3; ranking of components based on R values to identify those that pose the greatest threat to system reliability and require prioritized monitoring or optimization. Results: The results of the FMECA analysis indicate that the most critical components of the wind turbine system are: gearbox – the highest criticality level (R value), as gearbox failure can lead to complete system shutdown and costly repairs; generator – high severity of failure and moderate likelihood of failure detection; wind turbine control system – although failures are less frequent, the consequences can be severe due to the loss of control over the turbine. Based on the analysis, components have been classified according to maintenance and monitoring priorities to enable timely detection of potential failures and prevent major breakdowns. Conclusion: The FMECA method is an effective tool for identifying and ranking potentially critical components of wind turbine systems. The results indicate that the gearbox, generator, and control system are the most sensitive points in the system. Their preventive maintenance, along with the implementation of condition monitoring systems and design improvements, can significantly enhance reliability and reduce operational costs. The analysis can serve as a foundation for improving maintenance strategies and increasing wind farms operational efficiency.

As modern power systems increasingly integrate wind energy, the cybersecurity of wind turbine control systems becomes a critical concern. In particular, sensor attacks can compromise operational safety and lead to significant performance degradation. This paper introduces an innovative approach aimed at enhancing cybersecurity of wind turbines against such attacks. The proposed approach leverages an adaptive Kalman filter, augmented with a forgetting factor to dynamically adjust to changing conditions. The mathematical analysis of the adaptive Kalman filter's exponential stability is rigorously examined, under standard observability and controllability conditions. Through numerical simulations in two different scenarios, the effectiveness of the proposed approach is thoroughly evaluated. The results show that the adaptive Kalman filter achieves notable precision in estimating the actual magnitude of the attacks, even in the presence of noise. This resilience demonstrates the robustness of the proposed methodology in safeguarding wind turbine cybersecurity in dynamic and challenging environments.

In the pursuit of advancing renewable energy technologies, optimizing wind turbine performance through sophisticated control systems is pivotal for addressing energy variability and enhancing system efficiency. This paper presents a novel hybrid fuzzy super-twisting sliding mode control (HFSTSMC) strategy for Doubly Fed Induction Generators (DFIGs) used in modern wind turbine configurations. DFIGs are favored for their adaptability and efficiency in managing variable wind speeds, making them integral to sustainable energy solutions. The proposed HFSTSMC integrates the robustness of super-twisting sliding mode control (STSMC) with the adaptive capabilities of fuzzy logic control (FLC), aiming to mitigate the chattering effect inherent in traditional sliding mode controls while maintaining high tracking accuracy and robustness. The hybrid controller utilizes fuzzy logic to dynamically adjust control gains based on system states, enhancing the dynamic performance and stability of DFIG-based wind turbines. Extensive modeling and simulation using MATLAB/SIMULINK validate the effectiveness of HFSTSMC in improving power quality, reducing mechanical stresses, and optimizing energy capture. The results demonstrate significant improvements in system response to rapid wind condition changes and overall operational efficiency under transient and uncertain conditions. This research underscores the substantial benefits of implementing HFSTSMC, highlighting its potential to revolutionize wind turbine control systems and contribute significantly to the global transition towards reliable and efficient renewable energy sources.

As an important source of clean and renewable resources, the importance of wind power generation is increasing under the background of the theme of “environmental protection” in the new era. The efficiency of wind power generation is mainly affected by the reliability and performance of the power generation system, so it is necessary to use a single-chip microcomputer system and an intelligent algorithm to assist control, so as to improve the performance of wind power generation. Based on this research background, this paper expounds the working principle and basic characteristics of a single-chip microcomputer, summarizes its application advantages in wind turbine control, and puts forward the application practice strategy of a single-chip microcomputer in a wind turbine control system, which provides important assistance for the upgrading of the wind power system.

The DC over-voltage of the offshore wind HVDC system caused by onshore AC system faults is an important safety issue facing the absorption of offshore new energy. Existing methods generally involve adding energy-dissipation devices or communication links, as well as improving the LVRT strategy of wind turbines. However, these methods reduce engineering economy or reliability and require changes to the proven control system of wind turbines. Based on this, this paper proposes an FRT strategy for the onshore AC grid fault of offshore wind HVDC systems based on the iterative calculation method. Firstly, a calculation model for the maximum transmittable active power of the onshore MMC under onshore AC fault conditions considering the LVRT strategy is provided. Then, an equivalent power flow calculation model for the offshore MMC during voltage sag is established. Meanwhile, a maximum active power model for the PMSG considering the LVRT strategy is also established. Based on it, an iterative calculation method for the AC voltage reference value of offshore MMC is proposed. Finally, simulations conducted on the RTDS platform have confirmed the efficacy of the presented method. This paper represents a significant contribution to the field of offshore wind HVDC system operational safety, while also facilitating advancements in offshore wind energy development.

The 2025 International Conference on Renewable Energy and Energy Conservation (REEC 2025) marked a significant milestone in the global discourse on sustainable energy solutions. Held in-person from March 7-9, 2025, in the vibrant city of Xinyu, Jiangxi, China, the conference brought together researchers, engineers, policymakers, and industry leaders to exchange cutting-edge ideas and innovations. Against the backdrop of accelerating climate change and the urgent need for energy transition, REEC 2025 served as a dynamic platform to explore interdisciplinary advancements in renewable energy technologies, energy conservation strategies, and their integration into modern infrastructure. The event was hosted by the Jiangxi New Energy Technology Institute, China. The conference agenda was meticulously designed to foster knowledge sharing and collaboration. The Opening Ceremony set the tone with inspiring remarks on the global energy landscape, followed by a series of keynote speeches delivered by eminent scholars. Prof. Qinmin Yang (Zhejiang University) elucidated the frontiers of intelligent control systems for wind turbines, bridging theory and practical implementation. Prof. Wei Xu (Chinese Academy of Sciences) unveiled breakthroughs in linear oscillatory machines, highlighting their potential for high-efficiency energy conversion. Prof. Zhipeng Wang (Jiangxi Normal University) captivated the audience with his insights into vertical graphene’s role in supercapacitor technology, while Prof. Pin Jern Ker (Sunway University) provided a comprehensive overview of hybrid energy storage systems for microgrids. Closing the keynote sessions, Prof. Raffaele Carli (Polytechnic University of Bari) presented innovative decision-making frameworks for agro-voltaic systems, emphasizing their socio-economic impact on rural communities. These talks not only deepened theoretical understanding but also sparked discussions on scalable real-world applications. The oral presentation sessions further enriched the conference, featuring rigorously peer-reviewed research across four thematic tracks: Solar and Wind Energy Technologies, Energy Storage and Grid Integration, Policy and Economics of Renewable Energy, and Emerging Materials for Energy Applications. Presentations delved into topics such as hybrid energy systems, AI-driven energy management, and socio-technical barriers to adoption. The interactive Q&A sessions fostered lively debates, reflecting the conference’s commitment to collaborative problem-solving. The Closing Ceremony celebrated these contributions and reiterated the collective responsibility to translate research into actionable solutions. List of Committee Member are available in this PDF.

This research is dedicated to improving the control system of wind turbines (WT) to ensure optimal efficiency and rapid responsiveness. To achieve this, the fuzzy logic control (FLC) method is implemented to control the converter in the rotor side (RSC) of a doubly fed induction generator (DFIG) and its performance is compared with an optimized proportional integral (PI) controller. The study demonstrated an enhancement in the performance of the DFIG through the utilization of the proposed FLC, effectively overcoming limitations and deficiencies observed in the conventional controllers, this approach significantly improved the performance of the wind turbine. Additionally, the selected membership functions were found to be highly compatible with the unique characteristics of wind energy. The optimization process is implemented for the controllers of both the grid side converter (GSC) and RSC. Through simulated analyses conducted using MATLAB/Simulink software, comprehensive assessments are carried out. The robustness of the FLC is evaluated compared to the optimized controllers across various wind profiles and challenging fault conditions. The results demonstrate satisfactory performance of the FLC in terms of steady-state time, stability, and precision under diverse wind speed profiles. The FLC achieves a significantly better settling time than the enhanced PI, improving by approximately 14–70% under normal conditions and 40–70% under various fault conditions. Additionally, the FLC outperforms the enhanced PI in fault conditions by reducing peak-to-peak oscillations by about 30–65%. It also delivers a smaller steady-state error, with improvements of around 2–4% under both normal conditions and most fault scenarios.

ABSTRACTThe installed wind energy capacity increases every year. However, operation and maintenance costs still make up a considerable portion of the levelized costs of electricity. This costs can be greatly reduced by the application of suitable early fault detection methods. The supervisory control and data acquisition system of wind turbines is one key element, as nowadays it provides high resolution operating data. Such data can be used for the implementation of normal behavior models, one precursor for concise early fault detection methods. In this work, we implement a generally applicable and interpretable partly physics‐based, partly empirical normal behavior model for drive train temperatures in wind turbines. The model achieves a coefficient of determination well above 0.9 for 12 of 16 available temperature signals. We also present the possibility of interpreting the model's underlying physical correlations for a selected temperature signal. In addition, we evaluate the influence of the input data's temporal resolution on the model quality. While models based on 10 s data perform slightly better, the results show that 10 min data are sufficient for modeling temperatures.

This paper addresses the co-design problem of adaptive control for networked wind turbine systems that track the desired rotor speed while efficiently scheduling network communication. Unlike existing approaches, the proposed method integrates the control design and the communication considerations, ensuring asymptotic tracking of rotor speed under the generator torque saturation with significantly reduced communication load. Firstly, the communication scheme is developed using the dynamic event-triggering mechanism, which introduces the feedback signal in the sampling loop. Secondly, an auxiliary signal is designed to mitigate the negative effect of inevitable generator torque saturation, ensuring that the bounded controller asymptotically exits from saturation. Then, the adaptive torque controller is constructed with the compensation signals to guarantee asymptotic stability, and the measurement function for dynamic event-triggering is co-designed alongside the controller to regulate the sampling-induced error. Furthermore, the requirement for exact knowledge of the wind turbine systems is eliminated by utilizing an online approximator to learn the uncertain aerodynamics and parameters. Finally, it is theoretically proven that all the signals in the closed-loop system are bounded, and the rotor speed asymptotically tracks the reference rotor speed. The feasibility and advantages of the proposed method are demonstrated on the NREL 5-MW wind turbine using the high-fidelity OpenFAST simulation platform.

This paper proposes a dual-loop back-to-back converter coordination control scheme with a DC-side voltage as the primary control target, along with a CROW unloading control strategy for low voltage ride-through (LVRT) capability enhancement. The feasibility and effectiveness of the proposed system topology and control strategy are verified through MATLAB/Simulink simulations. Furthermore, a hybrid short-term wind power prediction model based on data-driven and deep learning techniques (CEEMDAN-CNN-Transformer-XGBoost) is introduced in the wind turbine control system. The coordination control strategy seamlessly integrates wind power prediction, pitch angle adjustment, and the control system, embodying a predictive-driven intelligent optimization control approach. This method significantly improves prediction accuracy and stability, theoretically reduces unnecessary pitch angle adjustments, lowers mechanical stress, and enhances system adaptability in complex operating conditions. The research findings provide a valuable theoretical foundation and technical reference for the intelligent and efficient operation of wind power generation systems.

An MPC-based fault tolerant control of wind turbines in the presence of simultaneous sensor and actuator faults

The increasing frequency of wind turbine failures due to extreme weather conditions necessitates the implementation of new solutions to enhance their operational reliability. This paper presents an automatic rotor drop system specifically designed for wind turbines equipped with the Onipko rotor. The system aims to protect turbines from damage caused by critical wind speeds, reducing maintenance costs and extending the equipment’s lifespan. The unique design of the Onipko rotor allows it to operate at wind speeds as low as 0.1 m/s. However, its high drag coefficient and lack of aerodynamic optimization make it susceptible to mechanical stress and structural instability under strong gusts, requiring additional protective measures. The paper presents a calculation of the critical wind speed at which protective measures must be initiated. Through mathematical modeling, this study demonstrates the effectiveness of the rotor drop system in ensuring safe operation at wind speeds reaching 23.5 m/s. The optimization of the PI controller parameters provides a rapid response and stability, significantly enhancing the resilience of wind turbines to adverse weather conditions.

Renewable energies have a fundamental role in sustainability, with wind power being one of the most important due to its low production costs. Modern wind turbines are becoming bigger and more complex, and their operation and maintenance must be as optimized as possible. In this context, supervisory control and data acquisition systems provide valuable information, but there is no precise methodology for their analysis. To overcome this need, a generalized methodology is proposed to determine the recognition of critical subsystems through alarm analysis and management. The proposed methodology defines each subsystem in a precise way, shows the indicators for the alarms, and presents a theoretical framework for its application using the quantity and activation times of alarms, along with the real downtime. It also considers the transition of states when the wind turbine is operationally inactive. To highlight the proposal’s novelty, the methodology is exemplified with a case study from the Southern Cone, applying the method through a data management and analysis tool. Four critical subsystems were found, with the alarms of wind vanes, anemometers, and emergency speeds being of relevance. The indicators and the graphical tools recommended helped guide the applied analysis.

The study addresses the challenge of bird collisions with wind turbines by developing an autonomous risk assessment method. The research uses data from the stereoscopic Bird Protection System (BPS) to anticipate potential collision threats by analysing flight parameters and distance from turbines. The danger factor depends on the flight characteristics of the identified bird species and the parameters of the wind turbine control system. The paper proposes an online quantitative risk assessment model that operates in real time, with the aim of minimising unnecessary turbine shutdowns while improving bird conservation. The model is validated through field data from bird flights. The findings suggest that adaptive management of turbine operations based on real-time bird flight data can significantly reduce collision risks without compromising energy production efficiency. The research underscores the balance between ecological considerations and the economic viability of wind energy, proposing an adaptive strategy that reduces unnecessary turbine stoppages while ensuring the safety of avian species.

The supervisory control and data acquisition (SCADA) system of wind turbines continuously collects a large amount of monitoring data during their operation. These data contain abundant information about the operating status of the turbine components. Utilizing this information makes it feasible to provide early warnings and predict the health status of the wind turbine. However, due to the strong coupling between the various components of the wind turbine, the data exhibits complex spatiotemporal relationships, multiple state parameters, strong non-linearity, and noise interference, which brings great difficulty to anomaly detection of the wind turbine. This paper proposes a new method for detecting abnormal operating conditions of wind turbines, based on a cleverly designed multi-layer linear residual module and the improved temporal convolutional network (TCN) with a new norm-linear-ConvNeXt architecture (NLC-TCN). Initially, the NLC-TCN deep learning reconstruction model is trained with historical data of normal behavior to extract the spatiotemporal features of state parameters under normal operational conditions. Subsequently, the condition score of the unit is determined by calculating the average normalized root mean square error between the reconstructed data and actual data. The streaming peaks-over-threshold real-time calculation of the anomaly warning threshold, based on extreme value theory, is then used for preliminary fault monitoring. Moreover, by shielding the fault alarm for low wind speeds and implementing a continuous delay perception mechanism, issues related to wind speed fluctuations and internal and external interference are addressed, enabling early warning for faulty units. Finally, the effectiveness and reliability of the proposed method are validated through comparative experiments using actual offshore wind farm SCADA data. The performance of the proposed method surpasses that of other compared methods. Additionally, the results of the proposed method were evaluated using the uniform manifold approximation and projection dimensionality reduction technique and kernel density estimation.

This research presents a novel fault-tolerant predictive power control method for a Doubly-fed induction generator (DFIG) used in wind turbine control systems. Due to the proposed control mechanism, the system can continue to function effectively despite open-circuit or short-circuit faults in the insulated-gate bipolar transistors (IGBTs) of the MPC controller. Depending on the type of problem and its location, the tolerant IGBT overcomes power oscillations and limits the power converter's potential switching states. By monitoring the optimal generator speed, wind turbine control systems strive to maximize power output. For wind turbines operating in the partial-load area, a fault-tolerant model predictive control strategy is recommended in order to achieve control goals despite disturbances, uncertainties, sensor, and actuator difficulties. A high order sliding mode observer (HOSMO) is used to evaluate both the actual states and sensor-faults at the same time. A high order sliding mode (HOSM) control strategy based on the MPC controller is used to regulate the speed of wind turbines in order to harness the wind's maximum power.

As wind turbine generator systems become more common in the modern power grid, the question of how to adequately protect them from cyber criminals has become a major theme in the development of new control systems. As such, artificial intelligence (AI) and machine learning (ML) algorithms have become major contributors to preventing, detecting, and mitigating cyber-attacks in the power system. In their current state, wind turbine generator systems are woefully unprepared for a coordinated and sophisticated cyber attack. With the implementation of the internet-of-things (IoT) devices in the power control network, cyber risks have increased exponentially. The literature shows the impact analysis and exploring detection techniques for cyber attacks on the wind turbine generator systems; however, almost no work on the mitigation of the adverse effects of cyber attacks on the wind turbine control systems has been reported. To overcome these limitations, this paper proposes implementing an AI-based converter controller, i.e., a multi-agent deep deterministic policy gradient (DDPG) method that can mitigate any adverse effects that communication delays or bad data could have on a grid-connected doubly fed induction generator (DFIG)-based wind turbine generator or wind farm. The performance of the proposed DDPG controller has been compared with that of a variable proportional–integral (VPI) control-based mitigation method. The proposed technique has been simulated and validated utilizing the MATLAB/Simulink software, version R2023A, to demonstrate the effectiveness of the proposed method. Also, the performance of the proposed DDPG method is better than that of the VPI method in mitigating the adverse impacts of cyber attacks on wind generator systems, which is validated by the plots and the root mean square error table found in the results section.

The design of variable speed wind turbine control systems is a hard challenge where a nonlinear multivariable process with high disturbances, restrictions and interaction between variables appears. Under that situation, a reliable and powerful control strategy is needed to achieve a maximum performance. In this paper a multivariable control scheme based on a torque controller and pitch angle controller for a VS-VP small wind turbine connected with a permanent-magnet DC generator (PMG) is proposed. Both controllers are tuned for operating in several wind speed regions. Simulation results show the robustness, effectiveness and benefits of the multivariable control strategy used for different operation situations. The multivariable controller is compared with other baseline control schemes such as LQG controller and standard switched controller.

The Challenge to stabilize the grid frequency increases with the increment of renewable energy resources. System inertia/frequency control is a significant concern for maintaining system stability with a fast response time during the load transition. This manuscript proposes surface-mounted permanent magnet synchronous generator (SPMSG) based wind energy conversion system (WECS) with a frequency support DC-link controlling loop and a converter protective DC-link voltage-controller frequency support system. To achieve power exchange, the frequency control system (FCS) for the SPMSG-based wind turbine supports the grid-side virtual inertia. The DC-link voltage is disturbed during the load transition to maintain the frequency, while the electrolytic capacitor requires extra care regarding the DC bus voltage. This manuscript incorporates the FCS system with a supercapacitor and dynamic voltage limiter to avoid additional care of the DC bus voltage. A detailed analysis of system frequency with the additional load increment, normal connected load decrement, and various fault scenarios has been done. The proposed system is compared with the existing virtual inertia support (VIS). The simulation results show that the proposed frequency control system-based VIS efficiently limits the frequency deviations & DC-link voltage. The results are verified in the OPAL-RT 4510 real-time simulator environment to ensure the efficacy of the proposed system.