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.
Highlights
Partial discharge (PD) often precedes insulation breakdown in power cables and cable accessories, which can result in cost-intensive repairs and possibly prolonged outages
An Fiber optic distributed acoustic sensing (FDAS) system based on improved -OTDR assisted by a weak fiber Bragg grating array is demonstrated for partial discharge (PD) detection; and a classification method for identifying different PD patterns from FDAS signals, including internal PD, corona PD, and surface PD, is proposed
It further demonstrates that the convolutional neural network (CNN) model, which we proposed, is more effective in classifying internal PD signal, corona PD signal, surface PD signal and noise than using hand-crafted feature pattern recognition (PR) methods
Summary
Partial discharge (PD) often precedes insulation breakdown in power cables and cable accessories, which can result in cost-intensive repairs and possibly prolonged outages. Fiber optic distributed acoustic sensing (FDAS) based on phase-sensitive optical time-domain reflectometry ( -OTDR) [9]–[11] is an attractive approach for distributed measurements of weak vibration. Q. Che et al.: PD Recognition Based on Optical Fiber Distributed Acoustic Sensing and a CNN prior knowledge about PD mechanism and signal-processing techniques to construct appropriate features. Che et al.: PD Recognition Based on Optical Fiber Distributed Acoustic Sensing and a CNN prior knowledge about PD mechanism and signal-processing techniques to construct appropriate features To address these problems, a convolutional neural network (CNN) model is proposed. An FDAS system based on improved -OTDR assisted by a weak fiber Bragg grating (wFBG) array is demonstrated for PD detection; and a classification method for identifying different PD patterns from FDAS signals, including internal PD, corona PD, and surface PD, is proposed.
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