Detecting surface discharge faults in switchgear by using hybrid model
Switchgear plays a crucial role in power systems, providing protection and control over electrical equipment. However, tracking (surface discharge) can lead to insulation degradation and switchgear failure, necessitating reliable and effective identification of tracking defects. In this paper, we pr...
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2024
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my.uniten.dspace-340112024-10-14T11:17:39Z Detecting surface discharge faults in switchgear by using hybrid model Alsumaidaee Y.A.M. Koh S.P. Yaw C.T. Tiong S.K. Chen C.P. 58648412900 22951210700 36560884300 15128307800 57883616100 1D-CNN-LSTM Energy Surface charge Switchgear faults Tracking Switchgear plays a crucial role in power systems, providing protection and control over electrical equipment. However, tracking (surface discharge) can lead to insulation degradation and switchgear failure, necessitating reliable and effective identification of tracking defects. In this paper, we propose a hybrid one-dimension convolutional neural network long short-term memory networks (1D-CNN-LSTM) model as a solution to this problem. Data from both time domain analysis (TDA) and frequency domain analysis (FDA) are utilized for model evaluation. The model achieved error-free accuracy of 100% in both TDA and FDA during the training, validation, and testing phases. The model�s performance is further assessed using performance measures and the visualization of accuracy and loss curves. The results show that the hybrid 1D-CNN-LSTM model works well to accurately find and classify surface discharge tracking defects in switchgear. The model offers precise and dependable fault identification, which has the potential to significantly enhance switchgear functionality. By enabling proactive maintenance and timely intervention, the proposed model contributes to the overall reliability and performance of switchgear in power systems. The findings of this research provide valuable insights for the design and implementation of advanced fault detection systems in switchgear applications. � 2023 Institute of Advanced Engineering and Science. All rights reserved. Final 2024-10-14T03:17:39Z 2024-10-14T03:17:39Z 2023 Article 10.11591/ijeecs.v32.i1.pp413-422 2-s2.0-85174191481 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174191481&doi=10.11591%2fijeecs.v32.i1.pp413-422&partnerID=40&md5=2ca14addd8c331a475ce140420228368 https://irepository.uniten.edu.my/handle/123456789/34011 32 1 413 422 All Open Access Gold Open Access Institute of Advanced Engineering and Science Scopus |
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1D-CNN-LSTM Energy Surface charge Switchgear faults Tracking |
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1D-CNN-LSTM Energy Surface charge Switchgear faults Tracking Alsumaidaee Y.A.M. Koh S.P. Yaw C.T. Tiong S.K. Chen C.P. Detecting surface discharge faults in switchgear by using hybrid model |
| description |
Switchgear plays a crucial role in power systems, providing protection and control over electrical equipment. However, tracking (surface discharge) can lead to insulation degradation and switchgear failure, necessitating reliable and effective identification of tracking defects. In this paper, we propose a hybrid one-dimension convolutional neural network long short-term memory networks (1D-CNN-LSTM) model as a solution to this problem. Data from both time domain analysis (TDA) and frequency domain analysis (FDA) are utilized for model evaluation. The model achieved error-free accuracy of 100% in both TDA and FDA during the training, validation, and testing phases. The model�s performance is further assessed using performance measures and the visualization of accuracy and loss curves. The results show that the hybrid 1D-CNN-LSTM model works well to accurately find and classify surface discharge tracking defects in switchgear. The model offers precise and dependable fault identification, which has the potential to significantly enhance switchgear functionality. By enabling proactive maintenance and timely intervention, the proposed model contributes to the overall reliability and performance of switchgear in power systems. The findings of this research provide valuable insights for the design and implementation of advanced fault detection systems in switchgear applications. � 2023 Institute of Advanced Engineering and Science. All rights reserved. |
| author2 |
58648412900 |
| author_facet |
58648412900 Alsumaidaee Y.A.M. Koh S.P. Yaw C.T. Tiong S.K. Chen C.P. |
| format |
Article |
| author |
Alsumaidaee Y.A.M. Koh S.P. Yaw C.T. Tiong S.K. Chen C.P. |
| author_sort |
Alsumaidaee Y.A.M. |
| title |
Detecting surface discharge faults in switchgear by using hybrid model |
| title_short |
Detecting surface discharge faults in switchgear by using hybrid model |
| title_full |
Detecting surface discharge faults in switchgear by using hybrid model |
| title_fullStr |
Detecting surface discharge faults in switchgear by using hybrid model |
| title_full_unstemmed |
Detecting surface discharge faults in switchgear by using hybrid model |
| title_sort |
detecting surface discharge faults in switchgear by using hybrid model |
| publisher |
Institute of Advanced Engineering and Science |
| publishDate |
2024 |
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1814061099966791680 |
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13.222552 |