A New Approach Based on Deep Features of Convolutional Neural Networks for Partial Discharge Detection in Power Systems

Abstract

Partial discharge (PD) faults often occur as a result of breakdowns in the insulation layer of insulated overhead conductors. PD faults can cause serious problems such as power outages or electrical fire accidents. In this paper, a new PD detection system based on spectral analysis, spectrogram analysis, deep learning algorithms, minimum redundancy-maximum relevance (mRMR) and ensemble machine learning (EML) is presented. In the process of extracting distinctive features in the frequency dimension, 1D spectral data and 2D spectrum data based on frequency-time are obtained from a raw PD signal. Fourier transform-based three-power spectral density analyzes and one spectrogram analysis are performed. Deep features are obtained by using pre-trained 1D convolutional neural network models for 1D spectral data and the pre-trained ResNet-50 model for 2D spectrogram data. The most effective features are determined by applying mRMR feature selection analysis to the obtained deep features. In the last stage, PD detection is performed by applying the selected deep features to the EML classifier. The performance of the proposed PD detection system are evaluated with the VSB common data set. According to the experimental results, the proposed deep feature approach based PD detection system has very high performance.