Research on Fault Diagnosis Algorithm of Small and Medium Circuit Breakers in Intelligent Monitoring System Based on Deep Learning

This study proposes an innovative fault diagnosis algorithm for small circuit breakers based on deep learning to improve the accuracy and response speed of fault diagnosis. Firstly, a deep neural network model suitable for small circuit breaker fault detection is designed, and feature extraction is performed for common fault types such as overload, short circuit and poor contact of small circuit breakers during operation. Of course, through model simulation and experimental verification, the influence of different algorithms on fault diagnosis accuracy is systematically analyzed. The simulation results show that the algorithm has significant improvements in accuracy and real-time performance, with a fault diagnosis accuracy of 98.7% and an average identification time of less than 500ms, which is significantly better than traditional methods. Furthermore, the results of data analysis show that the algorithm is stable and adaptable in different operating conditions. It is proved that the intelligent monitoring and control system based on the deep learning can effectively diagnose the fault of the small circuit breaker, and it can ensure the safety and stability of the intelligent distribution network.