Research on Fault Location Algorithm for Power System Based on Convolutional Neural Network

Traditional fault diagnosis and localization methods typically rely on linear models for power system fault detection, resulting in low positioning accuracy under complex fault scenarios. To address this, we propose a convolutional neural network-based fault localization algorithm for power systems. The method first employs the 3σ principle and median filtering to eliminate outliers and suppress noise from multi-source data, then combines normalization to achieve feature fusion and generate high-dimensional feature vectors. Next, we construct a convolutional neural network-based anomaly signal recognition model: Using 1D-CNN to extract local temporal features, 2D-CNN to capture spatiotemporal features, and implementing binary classification tasks for fault signal identification. Finally, Morlet wavelet transform is applied for time-frequency analysis of abnormal signals, combined with phase difference method for precise fault zone localization. Experimental results demonstrate that our proposed method achieves 100% diagnostic accuracy with a fault localization error of 5 mm, demonstrating high positioning precision.