基于电流-振动特征自适应融合的BWO-CNN-LSTM断路器故障预测方法

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中图分类号：TM561 文献标志码：A 文章编号：2097-3853（2025）03-0269-08

Abstract：In order to improve the reliability of circuit breaker fault prediction results，a BWO-CNN-LSTM fault prediction method based on adaptive fusion of current-vibration signal features is proposed.First，the vibration signal is processed by variational modal decomposition to extract features such as energy entropy， sample entropy and scatter entropy；and the key features of the coil current signal are also extracted，and the feature set of the currnt-vibration joint signal for the complete action process of the circuit breaker is constructed by feature adaptive fusion technology；subsequently，the CNN-LSTM fault prediction model optimized bythe beluga whale optimization is constructed to predict the diferent mechanical states of the circuit breaker.Results show that compared with other algorithms，the BWO-CNN-LSTM model significantly improves the fault prediction accuracy，effctively solving the problem of manual experience dependence in circuit breaker fault prediction.

Keywords： high-voltage circuit breaker; adaptive feature fusion； beluga whale optimization； CNN-LSTM;fault prediction

高压断路器作为电力系统不可或缺的保护和控制设备，在电网稳定安全运行中发挥着关键作用[1]。（剩余11127字）