Partial Discharge Diagnosis Using Semi-Supervised Learning and Complementary Labels in Gas-Insulated Switchgear
Deep neural networks have proven to be highly efficient in fault detection and classification using partial discharges (PDs) in gas-insulated switchgear (GIS). However, previous studies have not fully addressed the issue of limited labeled training data, which significantly impacts the performance o...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10945843/ |
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| author | Ho Trong Tai Young-Woo Youn Hyeon-Soo Choi Yong-Hwa Kim |
| author_facet | Ho Trong Tai Young-Woo Youn Hyeon-Soo Choi Yong-Hwa Kim |
| author_sort | Ho Trong Tai |
| collection | DOAJ |
| description | Deep neural networks have proven to be highly efficient in fault detection and classification using partial discharges (PDs) in gas-insulated switchgear (GIS). However, previous studies have not fully addressed the issue of limited labeled training data, which significantly impacts the performance of these models. Existing semi-supervised learning (SSL) approaches typically discard low-confidence pseudo-labels from unlabeled PD samples, because these unreliable labels can mislead the model. This gap in current research overlooks the potential value of low-confidence samples, which, despite their uncertain class, are unlikely to belong to the classes with the lowest probabilities. In this study, we aim to overcome this limitation by proposing a novel semi-supervised contrastive complementary learning (SCCL) method. Our SCCL approach generates a larger set of reliable negative pairs using complementary labels, allowing us to utilize the entire set of unlabeled PD samples effectively. We validate the feasibility of SCCL using phase-resolved PDs (PRPDs) and onsite noise data collected through an ultrahigh-frequency (UHF) PD measurement system. Experimental results indicate that the SCCL method achieves an impressive accuracy of 96.23% by effectively leveraging low-confidence unlabeled PD samples to improve classification performance in GIS under restricted labeling conditions. |
| format | Article |
| id | doaj-art-c1f2138a2beb459fb86bbeca4be4cc9e |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c1f2138a2beb459fb86bbeca4be4cc9e2025-08-20T03:17:44ZengIEEEIEEE Access2169-35362025-01-0113587225873410.1109/ACCESS.2025.355635310945843Partial Discharge Diagnosis Using Semi-Supervised Learning and Complementary Labels in Gas-Insulated SwitchgearHo Trong Tai0https://orcid.org/0009-0002-9089-5081Young-Woo Youn1https://orcid.org/0000-0002-4207-971XHyeon-Soo Choi2Yong-Hwa Kim3https://orcid.org/0000-0003-2183-5085Department of Computer Science and Information, Korea National University of Transportation, Uiwang-si, Gyeonggi-do, South KoreaSmart Grid Research Division, Korea Electrotechnology Research Institute, Gwangju-si, Republic of KoreaGenad System, Naju-si, Jeollanam-do, Republic of KoreaDepartment of Computer Science and Information, Korea National University of Transportation, Uiwang-si, Gyeonggi-do, South KoreaDeep neural networks have proven to be highly efficient in fault detection and classification using partial discharges (PDs) in gas-insulated switchgear (GIS). However, previous studies have not fully addressed the issue of limited labeled training data, which significantly impacts the performance of these models. Existing semi-supervised learning (SSL) approaches typically discard low-confidence pseudo-labels from unlabeled PD samples, because these unreliable labels can mislead the model. This gap in current research overlooks the potential value of low-confidence samples, which, despite their uncertain class, are unlikely to belong to the classes with the lowest probabilities. In this study, we aim to overcome this limitation by proposing a novel semi-supervised contrastive complementary learning (SCCL) method. Our SCCL approach generates a larger set of reliable negative pairs using complementary labels, allowing us to utilize the entire set of unlabeled PD samples effectively. We validate the feasibility of SCCL using phase-resolved PDs (PRPDs) and onsite noise data collected through an ultrahigh-frequency (UHF) PD measurement system. Experimental results indicate that the SCCL method achieves an impressive accuracy of 96.23% by effectively leveraging low-confidence unlabeled PD samples to improve classification performance in GIS under restricted labeling conditions.https://ieeexplore.ieee.org/document/10945843/Semi-supervised learning (SSL)complementary labelscontrastive learningfault diagnosisphase-resolved partial discharge (PRPD)gas-insulated switchgear (GIS) |
| spellingShingle | Ho Trong Tai Young-Woo Youn Hyeon-Soo Choi Yong-Hwa Kim Partial Discharge Diagnosis Using Semi-Supervised Learning and Complementary Labels in Gas-Insulated Switchgear IEEE Access Semi-supervised learning (SSL) complementary labels contrastive learning fault diagnosis phase-resolved partial discharge (PRPD) gas-insulated switchgear (GIS) |
| title | Partial Discharge Diagnosis Using Semi-Supervised Learning and Complementary Labels in Gas-Insulated Switchgear |
| title_full | Partial Discharge Diagnosis Using Semi-Supervised Learning and Complementary Labels in Gas-Insulated Switchgear |
| title_fullStr | Partial Discharge Diagnosis Using Semi-Supervised Learning and Complementary Labels in Gas-Insulated Switchgear |
| title_full_unstemmed | Partial Discharge Diagnosis Using Semi-Supervised Learning and Complementary Labels in Gas-Insulated Switchgear |
| title_short | Partial Discharge Diagnosis Using Semi-Supervised Learning and Complementary Labels in Gas-Insulated Switchgear |
| title_sort | partial discharge diagnosis using semi supervised learning and complementary labels in gas insulated switchgear |
| topic | Semi-supervised learning (SSL) complementary labels contrastive learning fault diagnosis phase-resolved partial discharge (PRPD) gas-insulated switchgear (GIS) |
| url | https://ieeexplore.ieee.org/document/10945843/ |
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