Intelligent Mechanical Fault Diagnosis Method for High-Voltage Circuit Breakers Based on Grey Wolf Optimization and Multi-Grained Cascade Forest Algorithms

Abstract

High-voltage circuit breakers (HVCBs) handle the important tasks of controlling and safeguarding electricity networks. In the case of insufficient data samples, improving the accuracy of the traditional HVCB mechanical fault diagnosis method is difficult, so it poses challenges in meeting performance requirements for mechanical fault diagnosis. In this study, a HVCB fault diagnosis method is introduced. It utilizes a combination of grey wolf optimization (GWO) and multi-grained cascade forest (gcForest) algorithms to resolve these issues and improve the accuracy of HVCB mechanical fault diagnosis. To simplify the original vibration signal, the input feature quantity for the fault diagnosis method is obtained by calculating the energy entropy of the wavelet packet decomposition. The GWO algorithm is employed to optimize the parameters of the gcForest model, leading to identification of the optimum parameter configuration. Subsequently, the diagnostic effect in the case of a small sample size was analyzed through a VS1 vacuum circuit breaker example, and the accuracy reached 95.89%. In the case of unbalanced samples, further analysis and comparison with different methods confirm the feasibility and efficiency of the combination of GWO and gcForest algorithms. This study provides an effective solution for the diagnosis of mechanical faults in HVCBs.