Partial Discharge Recognition Based on Optical Fiber Distributed Acoustic Sensing and a Convolutional Neural Network
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8778802/ |
| _version_ | 1818947278837121024 |
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| author | Qian Che Hongqiao Wen Xinyu Li Zhaoqiang Peng Keven P. Chen |
| author_facet | Qian Che Hongqiao Wen Xinyu Li Zhaoqiang Peng Keven P. Chen |
| author_sort | Qian Che |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T08:28:22Z |
| format | Article |
| id | doaj.art-2d838cb9f794467a8dcbe0ef096b8286 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T08:28:22Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2d838cb9f794467a8dcbe0ef096b82862022-12-21T19:46:46ZengIEEEIEEE Access2169-35362019-01-01710175810176410.1109/ACCESS.2019.29310408778802Partial Discharge Recognition Based on Optical Fiber Distributed Acoustic Sensing and a Convolutional Neural NetworkQian Che0https://orcid.org/0000-0002-2370-9173Hongqiao Wen1Xinyu Li2Zhaoqiang Peng3Keven P. Chen4National Engineering Laboratory for Fiber Optic Sensing Technology, Wuhan University of Technology, Wuhan, ChinaNational Engineering Laboratory for Fiber Optic Sensing Technology, Wuhan University of Technology, Wuhan, ChinaNational Engineering Laboratory for Fiber Optic Sensing Technology, Wuhan University of Technology, Wuhan, ChinaDepartment of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USADepartment of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USAFiber-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.https://ieeexplore.ieee.org/document/8778802/Distributed acoustic sensingweak fiber Bragg grating (wFBG) arraypartial discharge (PD) |
| spellingShingle | Qian Che Hongqiao Wen Xinyu Li Zhaoqiang Peng Keven P. Chen Partial Discharge Recognition Based on Optical Fiber Distributed Acoustic Sensing and a Convolutional Neural Network IEEE Access Distributed acoustic sensing weak fiber Bragg grating (wFBG) array partial discharge (PD) |
| title | Partial Discharge Recognition Based on Optical Fiber Distributed Acoustic Sensing and a Convolutional Neural Network |
| title_full | Partial Discharge Recognition Based on Optical Fiber Distributed Acoustic Sensing and a Convolutional Neural Network |
| title_fullStr | Partial Discharge Recognition Based on Optical Fiber Distributed Acoustic Sensing and a Convolutional Neural Network |
| title_full_unstemmed | Partial Discharge Recognition Based on Optical Fiber Distributed Acoustic Sensing and a Convolutional Neural Network |
| title_short | Partial Discharge Recognition Based on Optical Fiber Distributed Acoustic Sensing and a Convolutional Neural Network |
| title_sort | partial discharge recognition based on optical fiber distributed acoustic sensing and a convolutional neural network |
| topic | Distributed acoustic sensing weak fiber Bragg grating (wFBG) array partial discharge (PD) |
| url | https://ieeexplore.ieee.org/document/8778802/ |
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