Fault Identification and Localization of a Time−Frequency Domain Joint Impedance Spectrum of Cables Based on Deep Belief Networks
To improve the accuracy of shallow neural networks in processing complex signals and cable fault diagnosis, and to overcome the shortage of manual dependency and cable fault feature extraction, a deep learning method is introduced, and a time−frequency domain joint impedance spectrum is proposed for...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/2/684 |
| _version_ | 1797437318316949504 |
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| author | Qingzhu Wan Yimeng Li Runjiao Yuan Qinghai Meng Xiaoxue Li |
| author_facet | Qingzhu Wan Yimeng Li Runjiao Yuan Qinghai Meng Xiaoxue Li |
| author_sort | Qingzhu Wan |
| collection | DOAJ |
| description | To improve the accuracy of shallow neural networks in processing complex signals and cable fault diagnosis, and to overcome the shortage of manual dependency and cable fault feature extraction, a deep learning method is introduced, and a time−frequency domain joint impedance spectrum is proposed for cable fault identification and localization based on a deep belief network (DBN). Firstly, based on the distribution parameter model of power cables, we model and analyze the cables under normal operation and different fault types, and we obtain the headend input impedance spectrum and the headend input time−frequency domain impedance spectrum of cables under various operating conditions. The headend input impedance amplitude and phase of normal operation and different fault cables are extracted as the original input samples of the cable fault type identification model; the real part of the headend input time–frequency domain impedance of the fault cables is extracted as the original input samples of the cable fault location model. Then, the unsupervised pre−training and supervised inverse fine−tuning methods are used for automatically learning, training, and extracting the cable fault state features from the original input samples, and the DBN−based cable fault type recognition model and location model are constructed and used to realize the type recognition and location of cable faults. Finally, the proposed method is validated by simulation, and the results show that the method has good fault feature extraction capability and high fault type recognition and localization accuracy. |
| first_indexed | 2024-03-09T11:18:27Z |
| format | Article |
| id | doaj.art-a330ef35d0d0440caddad4730b8d4c13 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:18:27Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a330ef35d0d0440caddad4730b8d4c132023-12-01T00:25:48ZengMDPI AGSensors1424-82202023-01-0123268410.3390/s23020684Fault Identification and Localization of a Time−Frequency Domain Joint Impedance Spectrum of Cables Based on Deep Belief NetworksQingzhu Wan0Yimeng Li1Runjiao Yuan2Qinghai Meng3Xiaoxue Li4School of Electric and Control Engineering, North China University of Technology, Beijing100144, ChinaSchool of Electric and Control Engineering, North China University of Technology, Beijing100144, ChinaSchool of Electric and Control Engineering, North China University of Technology, Beijing100144, ChinaSchool of Electric and Control Engineering, North China University of Technology, Beijing100144, ChinaKey Account Division, Beijing Aerospace Data Stock Company National Big-Data Application Technology, Beijing100044, ChinaTo improve the accuracy of shallow neural networks in processing complex signals and cable fault diagnosis, and to overcome the shortage of manual dependency and cable fault feature extraction, a deep learning method is introduced, and a time−frequency domain joint impedance spectrum is proposed for cable fault identification and localization based on a deep belief network (DBN). Firstly, based on the distribution parameter model of power cables, we model and analyze the cables under normal operation and different fault types, and we obtain the headend input impedance spectrum and the headend input time−frequency domain impedance spectrum of cables under various operating conditions. The headend input impedance amplitude and phase of normal operation and different fault cables are extracted as the original input samples of the cable fault type identification model; the real part of the headend input time–frequency domain impedance of the fault cables is extracted as the original input samples of the cable fault location model. Then, the unsupervised pre−training and supervised inverse fine−tuning methods are used for automatically learning, training, and extracting the cable fault state features from the original input samples, and the DBN−based cable fault type recognition model and location model are constructed and used to realize the type recognition and location of cable faults. Finally, the proposed method is validated by simulation, and the results show that the method has good fault feature extraction capability and high fault type recognition and localization accuracy.https://www.mdpi.com/1424-8220/23/2/684deep belief networkcable fault locationfault type identificationheadend input impedance spectrumtime–frequency domain |
| spellingShingle | Qingzhu Wan Yimeng Li Runjiao Yuan Qinghai Meng Xiaoxue Li Fault Identification and Localization of a Time−Frequency Domain Joint Impedance Spectrum of Cables Based on Deep Belief Networks Sensors deep belief network cable fault location fault type identification headend input impedance spectrum time–frequency domain |
| title | Fault Identification and Localization of a Time−Frequency Domain Joint Impedance Spectrum of Cables Based on Deep Belief Networks |
| title_full | Fault Identification and Localization of a Time−Frequency Domain Joint Impedance Spectrum of Cables Based on Deep Belief Networks |
| title_fullStr | Fault Identification and Localization of a Time−Frequency Domain Joint Impedance Spectrum of Cables Based on Deep Belief Networks |
| title_full_unstemmed | Fault Identification and Localization of a Time−Frequency Domain Joint Impedance Spectrum of Cables Based on Deep Belief Networks |
| title_short | Fault Identification and Localization of a Time−Frequency Domain Joint Impedance Spectrum of Cables Based on Deep Belief Networks |
| title_sort | fault identification and localization of a time frequency domain joint impedance spectrum of cables based on deep belief networks |
| topic | deep belief network cable fault location fault type identification headend input impedance spectrum time–frequency domain |
| url | https://www.mdpi.com/1424-8220/23/2/684 |
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