End-to-End Partial Discharge Detection in Power Cables via Time-Domain Convolutional Neural Networks

The analysis of partial discharge (PD) signals has been identified as a standardized diagnostic tool in monitoring the condition of different electrical apparatus nowadays. In this paper, we propose a novel data-driven approach to detect PD pulses in power cables using one-dimensional convolutional neural networks (CNNs), a successful deep neural network approach. Applying this deep learning method, an end-to-end framework has been proposed considering the propagations of PD signals and noises in power cables. The proposed method uses PD pulses as input, automatically extracts meaningful features for waveforms of PD pulses, and finally detects PD. Most of the existing methods, which use traditional classifiers, such as support vector machines (SVMs) and multi-layer perceptron (MLP) have mainly focused on improving feature representation and extraction manually for this task. However, the proposed CNN-based detection algorithm captures important latent features for waveforms of PD pulses with the help of its automatic feature extraction capability from raw inputs. Our experimental results show that the proposed method is better than conventional SVMs and achieves 97.38% and 93.23% detection accuracies in end-to-end settings based on our theoretical model-generated and empirical real-world PD signals, respectively.