Partial Discharge Recognition Based on Optical Fiber Distributed Acoustic Sensing and a Convolutional Neural Network

Abstract

Fiber-optic distributed acoustic sensing (FDAS) with phase-sensitive optical time-domain reflectometry (Φ-OTDR) is a promising technique for high-sensitivity measurement. In this paper, an improved Φ-OTDR system with a weak fiber Bragg grating (wFBG) array for partial discharge (PD) detection in cross-linked polyethylene (XLPE) power cables is demonstrated; and an event recognition method based on a convolutional neural network (CNN) model is proposed to identify and classify different types of events, including internal PD, corona PD, surface PD, and noise. A multiscale wavelet decomposition and reconstruction method is used to extract PD signals and a two-dimensional spectral frame representation of the PD signals is obtained by the mel-frequency cepstrum coefficients (MFCC). The experimental results based on 1280 training samples and 832 test samples have demonstrated high values of precision, sensitivity, and specificity for each event (up to 96.3%, 96.4%, and 98.7%, respectively), which means that the combination of multiscale wavelet decomposition and reconstruction, the MFCC and CNN may be a promising event recognition method for the FDAS systems.