Abstract
Abstract The safe and stable operation of pumped storage units is related to the integrity of the power grid, and it is necessary to study the prediction of unit deterioration trends. Traditional degradation trend prediction methods are mostly for single channel, without considering the interaction between different channels of the shaft system. Therefore, a multi-channel degradation trend prediction model for pumped storage units shaft system is proposed. Firstly, the healthy model is obtained by constructing the mapping relationship between the working parameters and the swing of the shaft system through the fully connected network. Secondly, the deterioration trend sequence of each channel is calculated based on the historical monitoring data of each channel of the unit shaft system and the corresponding healthy model. Finally, the correlation coefficient analysis method is used to calculate the correlation between each channel, and the temporality of the degradation sequence of each channel and the correlation between channels are learned through the spatio-temporal graph convolutional network to realize the prediction of multi-channel degradation trends. The proposed model is verified by using the data of a pumped storage power station. The results show that the proposed model can accurately predict the multi-channel deterioration trend.
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