Safety Detection and Fault Diagnosis of Primary Equipment in Substations Based on Intelligent Monitoring

With the continuous expansion of the power grid, the safe operation of primary equipment in substations is crucial for the stability of the power system. This article proposes a safety detection and fault diagnosis method for primary equipment in substations based on intelligent monitoring. Through a layered architecture (sensor layer, transport layer, data processing layer, and diagnostic layer), the system collects real-time device operating parameters. After data preprocessing, feature extraction, and dimensionality reduction, it uses deep learning models (combining CNN and LSTM) to achieve fault classification and early warning. The experiment shows that this method is significantly better than traditional methods in terms of classification accuracy, precision, recall, and F1 score, and still has high diagnostic accuracy under low signal-to-noise ratio conditions, providing effective technical support for the safe operation and maintenance of substation equipment.