Mechanical Fault Diagnosis of Circuit Breaker Based on Multi-Strategy Particle Swarm Algorithm

High-voltage circuit breaker (CB) are the most important type of switching devices in the regional power grid. They have protection and isolation functions. They are responsible for the control and protection functions of the power system, and their safe and stable operation plays an important role. In order to diagnose the mechanical failure of high-voltage CB, a particle swarm algorithm is proposed in this paper, which significantly improves the efficiency of the detection process; at the same time, the wavelet-correlation dimension is used to extract the vibration signal of the high-voltage CB and analyze its characteristics, so as to effectively describe various fault states, which can better distinguish different operating states. The experimental results show that the particle swarm algorithm is used to optimize the performance of CB mechanical fault diagnosis, and the influence of nuclear parameters in the model on the accuracy of the classification model is verified. Therefore, the use of particle swarm algorithm to optimize the mechanical fault diagnosis of CB is of particular research value.