Prediction method of mechanical state of high-voltage circuit breakers based on LSTM-SVM

The mechanical condition prediction can realize the early warning of mechanical fault, which is of great sig-nificance to the safe operation of high-voltage circuit breaker (HVCB) and the enhancement of the reliability for the power system. However, at present, few researches for the mechanical state prediction of HVCBs are re-ported. This paper proposes a novel prediction method for the mechanical condition of HVCB based on long short-term memory (LSTM) neural network and support vector machine (SVM). Firstly, based on LSTM, break signal, contact travel, and coil current of HVCB are predicted. Then, the key mechanical characteristic param-eters are calculated by using the predicted results. Finally, using the predicted mechanical characteristic pa-rameters, the mechanical state of the circuit breaker is diagnosed based on the SVM model, to realize the mechanical state prediction. Research indicates that the proposed method can accurately predict HVCB me-chanical conditions and lay a foundation for realizing predictive maintenance of HVCB mechanical state.