A Comprehensive Review of Signal Processing and Machine Learning Technologies for UHF PD Detection and Diagnosis (II): Pattern Recognition Approaches
Partial discharge (PD) pattern recognition approaches are designed to identify the types or severities of the insulation defects within the high voltage equipment, which is vital for evaluating potential harmfulness and making follow-up maintenance plan. In recent years, many advanced machine learni...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10443433/ |
| _version_ | 1797289817614057472 |
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| author | Jiachuan Long Lijuan Xie Xianpei Wang Jun Zhang Bing Lu Chun Wei Dangdang Dai Guowei Zhu Meng Tian |
| author_facet | Jiachuan Long Lijuan Xie Xianpei Wang Jun Zhang Bing Lu Chun Wei Dangdang Dai Guowei Zhu Meng Tian |
| author_sort | Jiachuan Long |
| collection | DOAJ |
| description | Partial discharge (PD) pattern recognition approaches are designed to identify the types or severities of the insulation defects within the high voltage equipment, which is vital for evaluating potential harmfulness and making follow-up maintenance plan. In recent years, many advanced machine learning (ML) algorithms have been introduced to this field and achieved remarkable results. As the second one of the two-part papers, we aim to give a comprehensive review regarding the pattern recognition approaches for ultra-high frequency (UHF) PD data in this paper. These methods are grouped into three categories, which are the traditional ML-based PD type recognition, the deep learning-based (DL) PD type recognition, and PD severity assessment. Specifically, for the first topic, feature extraction methods, dimensionality reduction methods and classification methods are reviewed separately. For the second topic, many state-of-the-art DL methods are discussed, including the deep belief network (DBN), deep autoencoder network (DAN), convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), graph convolutional network (GCN), deep ensemble learning (DEL), etc. For the third topic, the relevant algorithms are also divided into the conventional ML-based ones and the DL-based ones, which are studied in detail respectively. Finally, a brief discussion about the application effects of the above technologies is given, and some future directions are suggested. This paper covers almost every aspect of the PD pattern recognition and highlights the latest progress, which can provide valuable references for scholars in this field. |
| first_indexed | 2024-03-07T19:11:29Z |
| format | Article |
| id | doaj.art-9b174e04044f4912a373a1878493997d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-07T19:11:29Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9b174e04044f4912a373a1878493997d2024-03-01T00:00:56ZengIEEEIEEE Access2169-35362024-01-0112298502989010.1109/ACCESS.2024.336886610443433A Comprehensive Review of Signal Processing and Machine Learning Technologies for UHF PD Detection and Diagnosis (II): Pattern Recognition ApproachesJiachuan Long0https://orcid.org/0000-0001-8320-3855Lijuan Xie1https://orcid.org/0009-0008-3322-2570Xianpei Wang2Jun Zhang3Bing Lu4Chun Wei5Dangdang Dai6Guowei Zhu7Meng Tian8School of Electronics and Information Engineering, Wuhan Donghu University, Wuhan, ChinaSchool of Electronics and Information Engineering, Wuhan Donghu University, Wuhan, ChinaElectronic Information School, Wuhan University, Wuhan, ChinaMetrology Department, China Electric Power Research Institute, Wuhan, ChinaMetrology Department, China Electric Power Research Institute, Wuhan, ChinaSchool of Electronics and Information Engineering, Wuhan Donghu University, Wuhan, ChinaState Grid Information & Communication Branch of Hubei Electric Power Company Ltd., Wuhan, ChinaState Grid Information & Communication Branch of Hubei Electric Power Company Ltd., Wuhan, ChinaSchool of Automation, Wuhan University of Technology, Wuhan, ChinaPartial discharge (PD) pattern recognition approaches are designed to identify the types or severities of the insulation defects within the high voltage equipment, which is vital for evaluating potential harmfulness and making follow-up maintenance plan. In recent years, many advanced machine learning (ML) algorithms have been introduced to this field and achieved remarkable results. As the second one of the two-part papers, we aim to give a comprehensive review regarding the pattern recognition approaches for ultra-high frequency (UHF) PD data in this paper. These methods are grouped into three categories, which are the traditional ML-based PD type recognition, the deep learning-based (DL) PD type recognition, and PD severity assessment. Specifically, for the first topic, feature extraction methods, dimensionality reduction methods and classification methods are reviewed separately. For the second topic, many state-of-the-art DL methods are discussed, including the deep belief network (DBN), deep autoencoder network (DAN), convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), graph convolutional network (GCN), deep ensemble learning (DEL), etc. For the third topic, the relevant algorithms are also divided into the conventional ML-based ones and the DL-based ones, which are studied in detail respectively. Finally, a brief discussion about the application effects of the above technologies is given, and some future directions are suggested. This paper covers almost every aspect of the PD pattern recognition and highlights the latest progress, which can provide valuable references for scholars in this field.https://ieeexplore.ieee.org/document/10443433/Partial dischargeultra-high frequencypattern recognitionmachine learningdeep learningtype recognition |
| spellingShingle | Jiachuan Long Lijuan Xie Xianpei Wang Jun Zhang Bing Lu Chun Wei Dangdang Dai Guowei Zhu Meng Tian A Comprehensive Review of Signal Processing and Machine Learning Technologies for UHF PD Detection and Diagnosis (II): Pattern Recognition Approaches IEEE Access Partial discharge ultra-high frequency pattern recognition machine learning deep learning type recognition |
| title | A Comprehensive Review of Signal Processing and Machine Learning Technologies for UHF PD Detection and Diagnosis (II): Pattern Recognition Approaches |
| title_full | A Comprehensive Review of Signal Processing and Machine Learning Technologies for UHF PD Detection and Diagnosis (II): Pattern Recognition Approaches |
| title_fullStr | A Comprehensive Review of Signal Processing and Machine Learning Technologies for UHF PD Detection and Diagnosis (II): Pattern Recognition Approaches |
| title_full_unstemmed | A Comprehensive Review of Signal Processing and Machine Learning Technologies for UHF PD Detection and Diagnosis (II): Pattern Recognition Approaches |
| title_short | A Comprehensive Review of Signal Processing and Machine Learning Technologies for UHF PD Detection and Diagnosis (II): Pattern Recognition Approaches |
| title_sort | comprehensive review of signal processing and machine learning technologies for uhf pd detection and diagnosis ii pattern recognition approaches |
| topic | Partial discharge ultra-high frequency pattern recognition machine learning deep learning type recognition |
| url | https://ieeexplore.ieee.org/document/10443433/ |
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