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| Wind turbine fault diagnostic model based on BES-ELM |
| WANG Jun1,2,XI Fang3,ZHOU Chuan1,2,CAI Yanfeng1,2,WANG Jie1,2,WANG Jincheng4,XU Chang4 |
| 1. Guangdong Kenuo Surveying Engineering Co., Ltd., Guangzhou, Guangdong 510663, China; 2. China Energy Group Guangdong Electric Power Design Institute Co., Ltd., Guangzhou, Guangdong 510663, China; 3. CCCC Smart City Co., Ltd., Guangzhou, Guangdong 510290, China; 4. College of Energy and Electrical Engineering, Hohai University, Nanjing, Jiangsu 211100, China |
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Abstract To address the problem that improper selection of relevant parameters in extreme learning machine(ELM)affects its diagnostic results and accuracy, this paper proposes to optimize the selection of weights and biases of the ELM based on bold eagle search(BES)algorithm, and to construct a wind turbine fault diagnosis model combined with vulture search algorithm optimization and extreme learning machine(BES-ELM). The relevant SCADA data of a wind turbine in a wind farm in four states, including generator overheating(S1), feeder failure(S2), converter cooling system failure(S3)and normal(S4)was preprocessed and features were selected to form a fault sample set, 80% of which was used as a training set and 20% as a test set. These fault samples were classified using stan-dard ELM, extreme learning machine model based on genetic optimization algorithm and particle swarm optimization algorithm, respectively. The results show that compared with the standard extreme learning machine, the model optimized by genetic algorithm and particle swarm algorithm, the diagnostic accuracy of the BES-ELM model reaches 98.75%, which effectively improves the accuracy of wind turbine fault diagnosis.
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Received: 06 January 2022
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