Wind turbine generator state monitoring based on flower pollination algorithm and neural network

Qiutong Wu; Xuanyu Song; Yinong Cai

doi:10.1117/12.2679542

18 July 2023 Wind turbine generator state monitoring based on flower pollination algorithm and neural network

Qiutong Wu, Xuanyu Song, Yinong Cai

Proceedings Volume 12722, Third International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2023); 1272247 (2023) https://doi.org/10.1117/12.2679542
Event: International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2023), 2023, Hangzhou, China

Abstract

In this paper, new technologies and algorithms such as machine learning and swarm intelligently optimize algorithm are introduced into the condition monitoring of wind turbines, and the method of temperature trend analysis is used to monitor the condition of wind turbines. Firstly, in order to solve the problems of slow convergence speed and easy to fall into local optimum during the training of wavelet neural network, an improved method of Flower Pollination Algorithm optimized wavelet neural network is proposed, and the temperature model of wind turbine is established, and the model is used to carry out temperature prediction, and then the state of the wind turbine is obtained by analyzing the temperature residual, so as to achieve the purpose of online temperature monitoring.

Citation Download Citation

Qiutong Wu, Xuanyu Song, and Yinong Cai "Wind turbine generator state monitoring based on flower pollination algorithm and neural network", Proc. SPIE 12722, Third International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2023), 1272247 (18 July 2023); https://doi.org/10.1117/12.2679542

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