A Hybrid Method for Identifying the Spring Energy Storage State of Operating Mechanism in Circuit Breakers

Robust spring energy state identification of the operating mechanism is of great significance for monitoring the overall performance of the circuit breakers. However, rapid monitoring of the spring energy storage state based on the acquired current signal during the service period has not yet been realized. To address this problem, this research put forward a hybrid method for spring energy storage state identification and successfully applied it to the operating mechanism of circuit breakers. In this method, the Gramian angular field (GAF) is employed to represent the dynamic characteristics evolution process. Furthermore, combined with a convolutional block attention module (CBAM) and residual network (ResNet), a hybrid method is proposed for identifying the spring energy storage state and finally verified in the circuit breaker experiment. Experimental results proved the extraordinary efficiency of the proposed method (the average F1-score is reported as 0.994). The research suggests that the use of GAF might provide a viable source for state identification of operating mechanisms in circuit breakers.