Wind Turbine Active Fault Tolerant Control Based on Backstepping Active Disturbance Rejection Control and a Neurofuzzy Detector

Hamza Assia (), Houari Merabet Boulouiha, William David Chicaiza, Juan Manuel Escaño, Abderrahmane Kacimi, José Luis Martínez-Ramos () and Mouloud Denai
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Hamza Assia: Departement of Electrical Engineering, Laboratory of Automation and Systems Analysis (LAAS), National Polytechnic School of Oran (Maurice Audin), Oran 31000, Algeria
Houari Merabet Boulouiha: Departement of Electrical Engineering, Laboratory of Automation and Systems Analysis (LAAS), National Polytechnic School of Oran (Maurice Audin), Oran 31000, Algeria
William David Chicaiza: Department of System Engineering and Automatic Control, University of Seville, 41092 Seville, Spain
Juan Manuel Escaño: Department of System Engineering and Automatic Control, University of Seville, 41092 Seville, Spain
Abderrahmane Kacimi: Department of Instrumentation Maintenance, Institute of Maintenance and Industrial Safety, Oran 31000, Algeria
José Luis Martínez-Ramos: Department of Electrical Engineering, University of Seville, 41092 Seville, Spain
Mouloud Denai: Department of Engineering and Technology, University of Hertfordshire, Hatfield AL10 9AB, UK

Energies, 2023, vol. 16, issue 14, 1-22

Abstract: Wind energy conversion systems have become an important part of renewable energy history due to their accessibility and cost-effectiveness. Offshore wind farms are seen as the future of wind energy, but they can be very expensive to maintain if faults occur. To achieve a reliable and consistent performance, modern wind turbines require advanced fault detection and diagnosis methods. The current research introduces a proposed active fault-tolerant control (AFTC) system that uses backstepping active disturbance rejection theory (BADRC) and an adaptive neurofuzzy system (ANFIS) detector in combination with principal component analysis (PCA) to compensate for system disturbances and maintain performance even when a generator actuator fault occurs. The simulation outcomes demonstrate that the suggested method successfully addresses the actuator generator torque failure problem by isolating the faulty actuator, providing a reliable and robust solution to prevent further damage. The neurofuzzy detector demonstrates outstanding performance in detecting false data in torque, achieving a precision of 90.20 % for real data and 100 % for false data. With a recall of 100 % , no false negatives were observed. The overall accuracy of 95.10 % highlights the detector’s ability to reliably classify data as true or false. These findings underscore the robustness of the detector in detecting false data, ensuring the accuracy and reliability of the application presented. Overall, the study concludes that BADRC and ANFIS detection and isolation can improve the reliability of offshore wind farms and address the issue of actuator generator torque failure.

Keywords: active fault-tolerant control; backstepping; active disturbance rejection control; adaptive neurofuzzy inference system; principal component analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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