Circuit Breaker Fault Diagnosis Method Based on Integrated OCSSA-VMD-CNN-BiLSTM Method

Abstract

The paper proposes a novel approach to diagnose circuit breaker faults by integrating OCSSA, VMD, CNN, and BiLSTM algorithms. Firstly, OCSSA optimizes the parameters of VMD to reduce noise interference. Then, VMD decomposes the circuit breaker operating signals into characteristic signals. Secondly, CNN captures mechanical fault characteristics from the decomposed signals. Finally, BiLSTM applies CNN’s results for feature classification. Experimental results show that combining improved VMD with the CNN-BiLSTM model achieves a fault classification accuracy rate of 99.5%, surpassing other traditional models such as VMD-CNN-BiLSTM and ELMD in accuracy and robustness.