Research on Local Automated Detection and Fault Identification of Switchgear Based on Deep Learning Algorithm

This study focuses on the crucial issue of partial automation detection and fault identification in switchgear, presenting an innovative approach based on deep learning algorithms. By integrating Deep Belief Networks, Long Short-Term Memory Networks, and improved Convolutional Neural Networks, an efficient DBN-LSTM deep learning model has been developed. This model adeptly captures the intricate features of switchgear operational states, enabling precise detection of partial discharge and accurate identification of fault types. Experimental results demonstrate that the proposed algorithm significantly outperforms traditional methods in terms of detection accuracy, fault recognition rate, and anti-interference capability. This achievement not only offers new technological support for preventive maintenance and reliability enhancement in power systems but also opens new avenues for the application of deep learning in industrial fields. The research enriches the theoretical framework of switchgear fault diagnosis and provides robust technical support for practical engineering applications.