Xiao Ju Yin1This email address is being protected from spambots. You need JavaScript enabled to view it., Qi Zheng Mu1, Li Zhou1, Bo Li3, Guo Ce Shao2, and Zhi Liang Du1
1Department of Renewable Energy, Shenyang Institute of Engineering, Shenyang 110136, China
2Liaoning Branch of CGN New Energy Investment (Shenzhen) Co. , LTD 110623,China
3Collage of Information, Shenyang Institute of Engineering, Shenyang 110136, China
Received: August 30, 2023 Accepted: February 23, 2024 Publication Date: March 23, 2024
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
The vibration of flexible towers of wind turbines can cause accidents or even the shutdown of wind turbines from time to time. Establishing a tower vibration prediction model can effectively predict the occurrence of accidents. Aiming at the problems of low prediction accuracy of the BP neural network and easy to fall into the local optimal solution, the BP neural network is optimized using the chimp optimization algorithm (ChOA). To confirm the algorithm’s feasibility, the 120m flexible tower data of a 2 MW wind turbine in a wind farm is simulated and analyzed, and the tower vibration prediction model is used to establish by optimizing the heterogeneous data from multiple sources through correlation analysis under different operating conditions of the wind turbine to find out the correlation variables affecting the vibration of the flexible tower. The results show that the ChOABP neural network has the best prediction effect under the rated wind speed, the root mean square error (RMSE) decreases by 12.1267, and the mean absolute error (MAE) decreases by 9.688, and the error-index decreases by more than the rated wind speed, which proves that the algorithm is better than the optimized BP neural network in rated wind speed.
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