Research on intelligent detection method of high-voltage circuit breaker based on PSO–CNN

Abstract

Through the intelligent detection of high-voltage circuit breakers, equipment faults can be detected and diagnosed in time, which can effectively prevent power accidents and ensure the safe operation of the power grid. In this paper, a detection method based on particle swarm optimization (PSO) and convolutional neural network (CNN) is proposed, and the parameter setting of CNN is optimized by PSO. The feature of vibration signal is extracted and the feature sample set is constructed. On this basis, the sample set is constructed and the experiment is carried out. The experimental results show that the proposed method has significant advantages in precision, recall rate, and F1 score compared with the traditional method; the precision can reach 0.926, the recall can reach 0.913, and the F1 score can reach 0.919. Future research could optimize the training process to reduce running time, as well as introduce more advanced data preprocessing techniques to improve data quality.