A monitoring and warning method for hydroelectric units based on KPCA and LSTM

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (24) : 287-294.

WANG Yongfei1, LI Xiaofei1, SUN Yuxin2, ZHANG Jian1, GUO Pengcheng2, WANG Renben1

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Abstract

The safe and stable operation of hydropower units is very important for the safety of power stations and regional power grids. An intelligent early warning method for hydropower units based on Kernel Principal Component Analysis (KPCA) and Long Short-Term Memory (LSTM) is proposed in this paper, which realizes that trend early war function based on multi-channel vibration signal fusion. Firstly, the multi-channel vibration signals of hydropower units are fused, and KPCA is used to compress the data to reduce the amount of information processing in the process of early warning. Furthermore, the early warning threshold is set according to the steady-state operation condition of the unit by the multivariate statistical process control method, and the early warning function based on data fusion is realized. Then, the prediction model of vibration fusion data is constructed based on LSTM, and the prediction function of future operation data is realized. In that end, the effectiveness of the propose method is verified by the given case with a R2 coefficient exceeding 0.97, and realize the state monitoring and alarm prediction. Compared to the KPCA-RNN and KPCA-Informer models, the proposed model demonstrates the best performance on the same experimental data.

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Hydropower units / Long and short time memory network / Kernel Principal Component Analysis / Warning threshold

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WANG Yongfei1, LI Xiaofei1, SUN Yuxin2, ZHANG Jian1, GUO Pengcheng2, WANG Renben1. A monitoring and warning method for hydroelectric units based on KPCA and LSTM[J]. Journal of Vibration and Shock, 2024, 43(24): 287-294

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