Fusing Electromagnetic Physics and Deep Learning for Intelligent Prediction of Vacuum Degree in Circuit Breakers

Abstract

Vacuum degree is a key parameter determining the breaking and insulation performance of vacuum circuit breakers (VCBs). Its deterioration over time can lead to performance failure. Therefore, achieving high-precision online monitoring of the vacuum degree is essential for ensuring the operational reliability of VCBs. This study employs electromagnetic radiation waves generated by vacuum discharge as indirect indicators of vacuum degree and investigates their physical mechanism and detectable lower limits under different conditions. On this basis, a classification model integrating temporal convolutional network (TCN), bidirectional gated recurrent unit (BiGRU), and an attention mechanism is developed to learn discharge event features from the waveforms and classify internal pressure states. Experimental results show that the method supports vacuum degree monitoring from $1.0\times 10^{-3}$ to 10 Pa, achieves 92% precision, and outperforms support vector machine (SVM), kernel extreme learning machine (KELM), and back propagation neural network (BP-NN). This approach provides a practical and efficient solution for real-time condition monitoring of VCBs.