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

被引:0
|
作者
Assia, Hamza [1 ]
Boulouiha, Houari Merabet [1 ]
Chicaiza, William David [2 ]
Escano, Juan Manuel [2 ]
Kacimi, Abderrahmane [3 ]
Martinez-Ramos, Jose Luis [4 ]
Denai, Mouloud [5 ]
机构
[1] Natl Polytech Sch Oran Maurice Audin, Dept Elect Engn, Lab Automation & Syst Anal LAAS, Oran 31000, Algeria
[2] Univ Seville, Dept Syst Engn & Automatic Control, Seville 41092, Spain
[3] Inst Maintenance & Ind Safety, Dept Instrumentat Maintenance, Oran 31000, Algeria
[4] Univ Seville, Dept Elect Engn, Seville 41092, Spain
[5] Univ Hertfordshire, Dept Engn & Technol, Hatfield AL10 9AB, England
基金
欧盟地平线“2020”;
关键词
active fault-tolerant control; backstepping; active disturbance rejection control; adaptive neurofuzzy inference system; principal component analysis; TRACKING;
D O I
10.3390/en16145455
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
收藏
页数:22
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