Real Time Monitoring Technology of Substation Equipment Status Based on Convolutional Neural Network

Detecting substation equipment failures at an early stage is challenging due to subtle abnormal changes that traditional monitoring methods may overlook. To address this, a convolutional neural network (CNN) algorithm model is proposed for real-time monitoring of transformers, circuit breakers, and switch devices in substation equipment. The study preprocesses sample data by considering factors such as grid harmonics, clutter, magnetic field, ripple, temperature, and humidity, which influence equipment operation. The CNN model's convolutional and subsampling layer parameters are optimized to balance feature extraction and model complexity. Experimental results show that the algorithm's loss value gradually decreases with iterations, stabilizing at 0.1000 after 100 epochs, with test accuracy reaching 1. However, some sample categories are misclassified. Comparative analysis with decision tree, Support Vector Machine (SVM), and k-Nearest Neighbors (KNN) algorithms demonstrates CNN's superior performance in accuracy, F1 score, false positive rate, training time, and parameter count. This study highlights CNN's potential for enhancing substation equipment monitoring systems.