Fault Detection and Isolation in Wind Turbines: Type-3 Fuzzy Logic Systems and Adaptive Random Search Learning
Zhou A. Zhu Z. Ghaderpour E. Shakibjoo A.D. Taghavifar H. Mohammadzadeh A. Zhang C.
2024Institute of Electrical and Electronics Engineers Inc.
IEEE Access
2024#12129347 - 129361 pp.
Ensuring the reliability of wind energy conversion systems (WECSs) is a crucial task for maximizing energy capture from the wind. A detailed model incorporating mechanical and electrical components is essential for accurately diagnosing system errors and assessing their impact on subsystems. Additionally, a fault detection and isolation system is necessary to quickly identify recurring faults and prevent significant economic losses. This study introduces a fault detection and isolation system using dynamic model of WECS based on type-3 (T3) fuzzy logic systems (FLSs). The adaptive random search (ARS) is employed to optimize the T3-FLS parameters and structure for enhanced fault detection accuracy. T3-FLSs handle higher levels of uncertainty and variability compared to traditional FLSs and neural networks. This allows for more accurate fault detection in complex and dynamic systems. One T3-FLS model replicates the systems normal operation, while another simulates faulty conditions. These T3-FLS models are run in parallel with the actual plant, allowing for comparison of their outputs with the real systems outputs to pinpoint error timing and location. The ARS is utilized to train the T3-FLSs, eliminating the need for gradient expression calculations. The appropriate number of rules for the T3-FLS is determined using Akaike and final prediction error criteria. Simulation results demonstrate the systems ability to rapidly detect and isolate errors with minimal false alarms. This research framework can be applied to identify errors in various system components effectively.
control systems , Fault detection , isolation , machine learning , type-3 fuzzy logic , wind energy
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Zhejiang Guangsha Vocational and Technical University of Construction, School of Intelligent Manufacturing, Dongyang, 322100, China
Zhejiang LINIX Motor Company Ltd., Dongyang, 322100, China
Sapienza University of Rome, CERI Research Centre, Department of Earth Sciences, Rome, 00185, Italy
Ahrar Institute of Technology and Higher Education, Department of Electrical Engineering, Rasht, 41931-63591, Iran
Concordia University, Department of Mechanical, Industrial and Aerospace Engineering, Montreal, H3G 1M8, QC, Canada
Astana IT University, Department of Computational and Data Science, Astana, 020000, Kazakhstan
Shenyang University of Technology, Shenyang Economic-Technological Area, Multidisciplinary Center for Infrastructure Engineering, Liaoning, Shenyang, 110870, China
Zhejiang Guangsha Vocational and Technical University of Construction
Zhejiang LINIX Motor Company Ltd.
Sapienza University of Rome
Ahrar Institute of Technology and Higher Education
Concordia University
Astana IT University
Shenyang University of Technology
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