A Deep Neural Network to Identify Vacuum Degrees in Vacuum Interrupter Based on Partial Discharge Diagnosis
One of the most crucial parameters in operating a vacuum interrupter (VI) is internal pressure. The failure of switching or insulation occurs when the pressure rises above a specific level. Characteristics of partial discharge (PD) in VI can be used to measure the internal pressures of VI. This pape...
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IEEE
2022-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/9831777/ |
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| author | Hong Nhung-Nguyen Young-Woo Youn Yong-Hwa Kim |
| author_facet | Hong Nhung-Nguyen Young-Woo Youn Yong-Hwa Kim |
| author_sort | Hong Nhung-Nguyen |
| collection | DOAJ |
| description | One of the most crucial parameters in operating a vacuum interrupter (VI) is internal pressure. The failure of switching or insulation occurs when the pressure rises above a specific level. Characteristics of partial discharge (PD) in VI can be used to measure the internal pressures of VI. This paper defines a classification problem for the degree of internal pressure in VI using PDs, which were measured using a capacitive PD coupler. Then, we propose a deep neural network to monitor the internal pressure of VI by analyzing PDs. Experimental results show that the proposed deep neural network monitors the internal pressure range, from <inline-formula> <tex-math notation="LaTeX">$1.0 \times 10^{-2}$ </tex-math></inline-formula> torr to 10 torr in VI. The classification performance of the proposed method is significantly better than those of machine learning algorithms such as support vector machines and <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-nearest neighbor algorithm and the proposed method achieves an 100% classification accuracy. |
| format | Article |
| id | doaj-art-12b35cdde863472cac5957ce0b8fc69c |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-12b35cdde863472cac5957ce0b8fc69c2025-01-17T00:00:22ZengIEEEIEEE Access2169-35362022-01-0110951259513110.1109/ACCESS.2022.31918059831777A Deep Neural Network to Identify Vacuum Degrees in Vacuum Interrupter Based on Partial Discharge DiagnosisHong Nhung-Nguyen0Young-Woo Youn1Yong-Hwa Kim2https://orcid.org/0000-0003-2183-5085Department of Electronic Engineering, Myongji University, Yongin, South KoreaSmart Grid Research Division, Korea Electrotechnology Research Institute, Changwon, South KoreaDepartment of Data Science, Korea National University of Transportation, Uiwang, South KoreaOne of the most crucial parameters in operating a vacuum interrupter (VI) is internal pressure. The failure of switching or insulation occurs when the pressure rises above a specific level. Characteristics of partial discharge (PD) in VI can be used to measure the internal pressures of VI. This paper defines a classification problem for the degree of internal pressure in VI using PDs, which were measured using a capacitive PD coupler. Then, we propose a deep neural network to monitor the internal pressure of VI by analyzing PDs. Experimental results show that the proposed deep neural network monitors the internal pressure range, from <inline-formula> <tex-math notation="LaTeX">$1.0 \times 10^{-2}$ </tex-math></inline-formula> torr to 10 torr in VI. The classification performance of the proposed method is significantly better than those of machine learning algorithms such as support vector machines and <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-nearest neighbor algorithm and the proposed method achieves an 100% classification accuracy.https://ieeexplore.ieee.org/document/9831777/Vacuum interrupterpartial dischargedeep neural network |
| spellingShingle | Hong Nhung-Nguyen Young-Woo Youn Yong-Hwa Kim A Deep Neural Network to Identify Vacuum Degrees in Vacuum Interrupter Based on Partial Discharge Diagnosis IEEE Access Vacuum interrupter partial discharge deep neural network |
| title | A Deep Neural Network to Identify Vacuum Degrees in Vacuum Interrupter Based on Partial Discharge Diagnosis |
| title_full | A Deep Neural Network to Identify Vacuum Degrees in Vacuum Interrupter Based on Partial Discharge Diagnosis |
| title_fullStr | A Deep Neural Network to Identify Vacuum Degrees in Vacuum Interrupter Based on Partial Discharge Diagnosis |
| title_full_unstemmed | A Deep Neural Network to Identify Vacuum Degrees in Vacuum Interrupter Based on Partial Discharge Diagnosis |
| title_short | A Deep Neural Network to Identify Vacuum Degrees in Vacuum Interrupter Based on Partial Discharge Diagnosis |
| title_sort | deep neural network to identify vacuum degrees in vacuum interrupter based on partial discharge diagnosis |
| topic | Vacuum interrupter partial discharge deep neural network |
| url | https://ieeexplore.ieee.org/document/9831777/ |
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