Fault detection for medium voltage switchgear using a deep learning hybrid 1D-CNN-LSTM model
Medium voltage (MV) switchgear is a vital part of modern power systems, responsible for regulating the flow of electrical power and ensuring the safety of equipment and personnel. However, switchgear can experience various types of faults that can compromise its reliability and safety. Common faults...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers Inc.
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/40640/ http://umpir.ump.edu.my/id/eprint/40640/1/Fault%20detection%20for%20medium%20voltage%20switchgear.pdf |
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| author | Alsumaidaee, Yaseen Ahmed Mohammed Paw, Johnny Koh Siaw Yaw, Chong Tak Tiong, Sieh Kiong Chen, Chai Phing Yusaf, Talal F. Benedict, Foo Kadirgama, Kumaran Hong, Tanchung Abd Alla, Ahmed N. |
| author_facet | Alsumaidaee, Yaseen Ahmed Mohammed Paw, Johnny Koh Siaw Yaw, Chong Tak Tiong, Sieh Kiong Chen, Chai Phing Yusaf, Talal F. Benedict, Foo Kadirgama, Kumaran Hong, Tanchung Abd Alla, Ahmed N. |
| author_sort | Alsumaidaee, Yaseen Ahmed Mohammed |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Medium voltage (MV) switchgear is a vital part of modern power systems, responsible for regulating the flow of electrical power and ensuring the safety of equipment and personnel. However, switchgear can experience various types of faults that can compromise its reliability and safety. Common faults in switchgear include arcing, tracking, corona, normal cases, and mechanical faults. Accurate detection of these faults is essential for maintaining the safety of MV switchgear. In this paper, we propose a novel approach for fault detection using a hybrid model (1D-CNN-LSTM) in both the time domain (TD) and frequency domain (FD). The proposed approach involves gathering a dataset of switchgear operation data and pre-processing it to prepare it for training. The hybrid model is then trained on this dataset, and its performance is evaluated in the testing phase. The results of the testing phase demonstrate the effectiveness of the hybrid model in detecting faults. The model achieved 100% accuracy in both the time and frequency domains for classifying faults in Switchgear, including arcing, tracking, and mechanical faults. Additionally, the model achieved 98.4% accuracy in detecting corona faults in the TD. The hybrid model proposed in this study provides an effective and efficient approach for fault detection in MV switchgear. By learning spatial and temporal features simultaneously, this model can accurately classify faults in both the TD and FD. This approach has significant potential to improve the safety of MV switchgear as well as other industrial applications. |
| first_indexed | 2025-11-15T03:39:32Z |
| format | Article |
| id | ump-40640 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:39:32Z |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-406402024-04-30T06:39:55Z http://umpir.ump.edu.my/id/eprint/40640/ Fault detection for medium voltage switchgear using a deep learning hybrid 1D-CNN-LSTM model Alsumaidaee, Yaseen Ahmed Mohammed Paw, Johnny Koh Siaw Yaw, Chong Tak Tiong, Sieh Kiong Chen, Chai Phing Yusaf, Talal F. Benedict, Foo Kadirgama, Kumaran Hong, Tanchung Abd Alla, Ahmed N. T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics Medium voltage (MV) switchgear is a vital part of modern power systems, responsible for regulating the flow of electrical power and ensuring the safety of equipment and personnel. However, switchgear can experience various types of faults that can compromise its reliability and safety. Common faults in switchgear include arcing, tracking, corona, normal cases, and mechanical faults. Accurate detection of these faults is essential for maintaining the safety of MV switchgear. In this paper, we propose a novel approach for fault detection using a hybrid model (1D-CNN-LSTM) in both the time domain (TD) and frequency domain (FD). The proposed approach involves gathering a dataset of switchgear operation data and pre-processing it to prepare it for training. The hybrid model is then trained on this dataset, and its performance is evaluated in the testing phase. The results of the testing phase demonstrate the effectiveness of the hybrid model in detecting faults. The model achieved 100% accuracy in both the time and frequency domains for classifying faults in Switchgear, including arcing, tracking, and mechanical faults. Additionally, the model achieved 98.4% accuracy in detecting corona faults in the TD. The hybrid model proposed in this study provides an effective and efficient approach for fault detection in MV switchgear. By learning spatial and temporal features simultaneously, this model can accurately classify faults in both the TD and FD. This approach has significant potential to improve the safety of MV switchgear as well as other industrial applications. Institute of Electrical and Electronics Engineers Inc. 2023 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/40640/1/Fault%20detection%20for%20medium%20voltage%20switchgear.pdf Alsumaidaee, Yaseen Ahmed Mohammed and Paw, Johnny Koh Siaw and Yaw, Chong Tak and Tiong, Sieh Kiong and Chen, Chai Phing and Yusaf, Talal F. and Benedict, Foo and Kadirgama, Kumaran and Hong, Tanchung and Abd Alla, Ahmed N. (2023) Fault detection for medium voltage switchgear using a deep learning hybrid 1D-CNN-LSTM model. IEEE Access, 11. pp. 97574-97589. ISSN 2169-3536. (Published) https://doi.org/10.1109/ACCESS.2023.3294093 https://doi.org/10.1109/ACCESS.2023.3294093 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics Alsumaidaee, Yaseen Ahmed Mohammed Paw, Johnny Koh Siaw Yaw, Chong Tak Tiong, Sieh Kiong Chen, Chai Phing Yusaf, Talal F. Benedict, Foo Kadirgama, Kumaran Hong, Tanchung Abd Alla, Ahmed N. Fault detection for medium voltage switchgear using a deep learning hybrid 1D-CNN-LSTM model |
| title | Fault detection for medium voltage switchgear using a deep learning hybrid 1D-CNN-LSTM model |
| title_full | Fault detection for medium voltage switchgear using a deep learning hybrid 1D-CNN-LSTM model |
| title_fullStr | Fault detection for medium voltage switchgear using a deep learning hybrid 1D-CNN-LSTM model |
| title_full_unstemmed | Fault detection for medium voltage switchgear using a deep learning hybrid 1D-CNN-LSTM model |
| title_short | Fault detection for medium voltage switchgear using a deep learning hybrid 1D-CNN-LSTM model |
| title_sort | fault detection for medium voltage switchgear using a deep learning hybrid 1d-cnn-lstm model |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics |
| url | http://umpir.ump.edu.my/id/eprint/40640/ http://umpir.ump.edu.my/id/eprint/40640/ http://umpir.ump.edu.my/id/eprint/40640/ http://umpir.ump.edu.my/id/eprint/40640/1/Fault%20detection%20for%20medium%20voltage%20switchgear.pdf |