This paper presents an artificial intelligence-based approach for monitoring and diagnosing equipment faults in substations. The study focuses on utilizing machine learning and deep learning algorithms such as Support Vector Machine (SVM),Residual Convolutional Neural Network (ResNet),and Long Short-Term Memory network (LSTM) to address the challenges of timeliness and accuracy in substation equipment monitoring. Experimental validations demonstrate the advantages of these algorithms in enhancing fault detection accuracy and real-time monitoring capabilities. This research not only showcases the potential applications of artificial intelligence in power systems but also provides a theoretical and practical foundation for the intelligent monitoring and maintenance of substations.
维普
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