Self-Attention Network for Partial-Discharge Diagnosis in Gas-Insulated Switchgear
Detecting, measuring, and classifying partial discharges (PDs) are important tasks for assessing the condition of insulation systems used in different electrical equipment. Owing to the implementation of the phase-resolved PD (PRPD) as a sequence input, an existing method that processes sequential d...
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| Format: | Article |
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MDPI AG
2020-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/8/2102 |
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| author | Vo-Nguyen Tuyet-Doan Tien-Tung Nguyen Minh-Tuan Nguyen Jong-Ho Lee Yong-Hwa Kim |
| author_facet | Vo-Nguyen Tuyet-Doan Tien-Tung Nguyen Minh-Tuan Nguyen Jong-Ho Lee Yong-Hwa Kim |
| author_sort | Vo-Nguyen Tuyet-Doan |
| collection | DOAJ |
| description | Detecting, measuring, and classifying partial discharges (PDs) are important tasks for assessing the condition of insulation systems used in different electrical equipment. Owing to the implementation of the phase-resolved PD (PRPD) as a sequence input, an existing method that processes sequential data, e.g., the recurrent neural network, using a long short-term memory (LSTM) has been applied for fault classification. However, the model performance is not further improved because of the lack of supporting parallel computation and the inability to recognize the relevance of all inputs. To overcome these two drawbacks, we propose a novel deep-learning model in this study based on a self-attention mechanism to classify the PD patterns in a gas-insulated switchgear (GIS). The proposed model uses a self-attention block that offers the advantages of simultaneous computation and selective focusing on parts of the PRPD signals and a classification block to finally classify faults in the GIS. Moreover, the combination of LSTM and self-attention is considered for comparison purposes. The experimental results show that the proposed method achieves performance superiority compared with the previous neural networks, whereas the model complexity is significantly reduced. |
| first_indexed | 2024-03-10T20:17:12Z |
| format | Article |
| id | doaj.art-42cf9c7d8112447fa91cc0b5bdb71e72 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T20:17:12Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-42cf9c7d8112447fa91cc0b5bdb71e722023-11-19T22:27:41ZengMDPI AGEnergies1996-10732020-04-01138210210.3390/en13082102Self-Attention Network for Partial-Discharge Diagnosis in Gas-Insulated SwitchgearVo-Nguyen Tuyet-Doan0Tien-Tung Nguyen1Minh-Tuan Nguyen2Jong-Ho Lee3Yong-Hwa Kim4Department of Electronic Engineering, Myongji University, Yongin 17058, KoreaDepartment of Electronic Engineering, Myongji University, Yongin 17058, KoreaDepartment of Electronic Engineering, Myongji University, Yongin 17058, KoreaSchool of Electronic Engineering, Soongsil University, Seoul 06978, KoreaDepartment of Electronic Engineering, Myongji University, Yongin 17058, KoreaDetecting, measuring, and classifying partial discharges (PDs) are important tasks for assessing the condition of insulation systems used in different electrical equipment. Owing to the implementation of the phase-resolved PD (PRPD) as a sequence input, an existing method that processes sequential data, e.g., the recurrent neural network, using a long short-term memory (LSTM) has been applied for fault classification. However, the model performance is not further improved because of the lack of supporting parallel computation and the inability to recognize the relevance of all inputs. To overcome these two drawbacks, we propose a novel deep-learning model in this study based on a self-attention mechanism to classify the PD patterns in a gas-insulated switchgear (GIS). The proposed model uses a self-attention block that offers the advantages of simultaneous computation and selective focusing on parts of the PRPD signals and a classification block to finally classify faults in the GIS. Moreover, the combination of LSTM and self-attention is considered for comparison purposes. The experimental results show that the proposed method achieves performance superiority compared with the previous neural networks, whereas the model complexity is significantly reduced.https://www.mdpi.com/1996-1073/13/8/2102fault diagnosisgas-insulated switchgear (GIS)long short-term memory (LSTM)partial discharges (PDs)self-attention |
| spellingShingle | Vo-Nguyen Tuyet-Doan Tien-Tung Nguyen Minh-Tuan Nguyen Jong-Ho Lee Yong-Hwa Kim Self-Attention Network for Partial-Discharge Diagnosis in Gas-Insulated Switchgear Energies fault diagnosis gas-insulated switchgear (GIS) long short-term memory (LSTM) partial discharges (PDs) self-attention |
| title | Self-Attention Network for Partial-Discharge Diagnosis in Gas-Insulated Switchgear |
| title_full | Self-Attention Network for Partial-Discharge Diagnosis in Gas-Insulated Switchgear |
| title_fullStr | Self-Attention Network for Partial-Discharge Diagnosis in Gas-Insulated Switchgear |
| title_full_unstemmed | Self-Attention Network for Partial-Discharge Diagnosis in Gas-Insulated Switchgear |
| title_short | Self-Attention Network for Partial-Discharge Diagnosis in Gas-Insulated Switchgear |
| title_sort | self attention network for partial discharge diagnosis in gas insulated switchgear |
| topic | fault diagnosis gas-insulated switchgear (GIS) long short-term memory (LSTM) partial discharges (PDs) self-attention |
| url | https://www.mdpi.com/1996-1073/13/8/2102 |
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