ANFIS based sound vibration combined fault diagnosis of high voltage circuit breaker (HVCB)
HVCB is an essential component of any power system. Finding its fault early on is beneficial to maintaining the stability of the power system. The combined acoustic and vibration signal generated by the action of mechanical components of an HVCB, as a homologous signal, can more effectively reflect...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-05-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722027329 |
| _version_ | 1827931838926028800 |
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| author | Zehua Chen Kuan Zhang Liyuan Yang Yongchun Liang |
| author_facet | Zehua Chen Kuan Zhang Liyuan Yang Yongchun Liang |
| author_sort | Zehua Chen |
| collection | DOAJ |
| description | HVCB is an essential component of any power system. Finding its fault early on is beneficial to maintaining the stability of the power system. The combined acoustic and vibration signal generated by the action of mechanical components of an HVCB, as a homologous signal, can more effectively reflect the state information of an HVCB in the event of a fault than a single signal. ANFIS fault diagnosis model composed of fuzzy theory in a neural network is proposed to address the problem of low accuracy in the field of fault diagnosis of the HVCB. Firstly, the noise reduction of the acoustic vibration signal is optimized based on wavelet analysis and CS-VMD, the local minimum envelope entropy is extracted, and the entropy weight method is used to fuse the local minimum envelope entropy of the acoustic vibration signal to form a composite feature vector, and the ANFIS fault diagnosis model is established. The experimental results show that the fault diagnosis method proposed in this paper has a low error in diagnosis results, and the accuracy has been greatly improved compared with the traditional diagnosis method. The eigenvector composed of sound vibration combined signal can more accurately reflect the operating state of the HVCB. |
| first_indexed | 2024-03-13T06:59:34Z |
| format | Article |
| id | doaj.art-e479c40ae2464f23bfd036f5eac988bd |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-03-13T06:59:34Z |
| publishDate | 2023-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-e479c40ae2464f23bfd036f5eac988bd2023-06-07T04:48:43ZengElsevierEnergy Reports2352-48472023-05-019286294ANFIS based sound vibration combined fault diagnosis of high voltage circuit breaker (HVCB)Zehua Chen0Kuan Zhang1Liyuan Yang2Yongchun Liang3Hebei University of Science and Technology, No. 26 Yuxiang Street, Yuhua District, Shijiazhuang City, Hebei Province, 050000, ChinaHebei University of Science and Technology, No. 26 Yuxiang Street, Yuhua District, Shijiazhuang City, Hebei Province, 050000, ChinaHebei University of Science and Technology, No. 26 Yuxiang Street, Yuhua District, Shijiazhuang City, Hebei Province, 050000, ChinaCorresponding author.; Hebei University of Science and Technology, No. 26 Yuxiang Street, Yuhua District, Shijiazhuang City, Hebei Province, 050000, ChinaHVCB is an essential component of any power system. Finding its fault early on is beneficial to maintaining the stability of the power system. The combined acoustic and vibration signal generated by the action of mechanical components of an HVCB, as a homologous signal, can more effectively reflect the state information of an HVCB in the event of a fault than a single signal. ANFIS fault diagnosis model composed of fuzzy theory in a neural network is proposed to address the problem of low accuracy in the field of fault diagnosis of the HVCB. Firstly, the noise reduction of the acoustic vibration signal is optimized based on wavelet analysis and CS-VMD, the local minimum envelope entropy is extracted, and the entropy weight method is used to fuse the local minimum envelope entropy of the acoustic vibration signal to form a composite feature vector, and the ANFIS fault diagnosis model is established. The experimental results show that the fault diagnosis method proposed in this paper has a low error in diagnosis results, and the accuracy has been greatly improved compared with the traditional diagnosis method. The eigenvector composed of sound vibration combined signal can more accurately reflect the operating state of the HVCB.http://www.sciencedirect.com/science/article/pii/S2352484722027329HVCBAcoustic vibration signalCS-VMDEnvelope entropyEntropy weight methodANFIS |
| spellingShingle | Zehua Chen Kuan Zhang Liyuan Yang Yongchun Liang ANFIS based sound vibration combined fault diagnosis of high voltage circuit breaker (HVCB) Energy Reports HVCB Acoustic vibration signal CS-VMD Envelope entropy Entropy weight method ANFIS |
| title | ANFIS based sound vibration combined fault diagnosis of high voltage circuit breaker (HVCB) |
| title_full | ANFIS based sound vibration combined fault diagnosis of high voltage circuit breaker (HVCB) |
| title_fullStr | ANFIS based sound vibration combined fault diagnosis of high voltage circuit breaker (HVCB) |
| title_full_unstemmed | ANFIS based sound vibration combined fault diagnosis of high voltage circuit breaker (HVCB) |
| title_short | ANFIS based sound vibration combined fault diagnosis of high voltage circuit breaker (HVCB) |
| title_sort | anfis based sound vibration combined fault diagnosis of high voltage circuit breaker hvcb |
| topic | HVCB Acoustic vibration signal CS-VMD Envelope entropy Entropy weight method ANFIS |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722027329 |
| work_keys_str_mv | AT zehuachen anfisbasedsoundvibrationcombinedfaultdiagnosisofhighvoltagecircuitbreakerhvcb AT kuanzhang anfisbasedsoundvibrationcombinedfaultdiagnosisofhighvoltagecircuitbreakerhvcb AT liyuanyang anfisbasedsoundvibrationcombinedfaultdiagnosisofhighvoltagecircuitbreakerhvcb AT yongchunliang anfisbasedsoundvibrationcombinedfaultdiagnosisofhighvoltagecircuitbreakerhvcb |