Deep learning approach for cable partial discharge pattern identification

Ensuring the durability of high-voltage (HV) cross-linked polyethylene (XLPE) cable insulation requires vigilant cleanliness maintenance during production to mitigate impurities, including oxidized parts and voids, which can compromise insulation integrity. Hence, this paper presents a MATLAB/Simulink partial discharge (PD) capacitive model of five well-known PD defects: crack, contamination, air void, microcrack, and composite, found in insulation materials HV XLPE insulation. Furthermore, this work proposes an extraordinary deep learning approach utilizing short-time Fourier transform (STFT) scalograms to represent PD signals in the time-frequency domain and train a convolutional neural network (CNN) to classify different PD defects. We focused on vital factors affecting STFT + CNN-aided pattern recognition accuracy, such as the number of network layers, convolutional kernel size, activation function, and pooling technique to optimize the network. Our study demonstrates that the proposed STFT + CNN approach outperforms traditional methods in recognizing PD patterns, especially for high signal similarity. Simulation results indicate that the STFT + CNN model achieves the highest classification accuracy of 0.9744 with minimal computation time (20 msec), making it suitable for real-time PD activity classification.