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<p>Wind energy systems have experienced significant advancements in recent years. However, their efficiency and reliability remain limited due to the increasing complexity of wind turbines and their deployment in remote locations, especially offshore. While many studies have focused on developing fault detection and control strategies, fault-tolerant control systems for wind turbines remain underexplored. This paper presents a model-based framework for the detection, localization, and isolation of faults in a 5&#xa0;MW wind turbine. A detailed simulation model was developed in the MATLAB/Simulink environment, covering a comprehensive range of fault scenarios affecting various subsystems, including sensors and actuators. The results reveal realistic detection challenges, such as sensitivity to small gain variations and actuator dynamics, which are often overlooked in idealized models. Our proposed approach integrates empirical modeling and diagnostic algorithms to better reflect real-world variability and system uncertainties. The study contributes a validated simulation platform and a fault database that can support future research in robust and scalable FDI techniques for large-scale wind turbines.</p>

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Advanced Fault Identification in Wind Energy Systems: A Model-Based Approach

  • 期刊论文

  • 发表日期：

    2025年11月3日

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  • Bouaziz Laila,
  • Dhaoui Mehdi,
  • Ben Hamed Mouna

摘要

Wind energy systems have experienced significant advancements in recent years. However, their efficiency and reliability remain limited due to the increasing complexity of wind turbines and their deployment in remote locations, especially offshore. While many studies have focused on developing fault detection and control strategies, fault-tolerant control systems for wind turbines remain underexplored. This paper presents a model-based framework for the detection, localization, and isolation of faults in a 5 MW wind turbine. A detailed simulation model was developed in the MATLAB/Simulink environment, covering a comprehensive range of fault scenarios affecting various subsystems, including sensors and actuators. The results reveal realistic detection challenges, such as sensitivity to small gain variations and actuator dynamics, which are often overlooked in idealized models. Our proposed approach integrates empirical modeling and diagnostic algorithms to better reflect real-world variability and system uncertainties. The study contributes a validated simulation platform and a fault database that can support future research in robust and scalable FDI techniques for large-scale wind turbines.