Self-Attention Network for Partial-Discharge Diagnosis in Gas-Insulated Switchgear

Vo-Nguyen Tuyet-Doan, Tien-Tung Nguyen, Minh-Tuan Nguyen, Jong-Ho Lee and Yong-Hwa Kim
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Vo-Nguyen Tuyet-Doan: Department of Electronic Engineering, Myongji University, Yongin 17058, Korea
Tien-Tung Nguyen: Department of Electronic Engineering, Myongji University, Yongin 17058, Korea
Minh-Tuan Nguyen: Department of Electronic Engineering, Myongji University, Yongin 17058, Korea
Jong-Ho Lee: School of Electronic Engineering, Soongsil University, Seoul 06978, Korea
Yong-Hwa Kim: Department of Electronic Engineering, Myongji University, Yongin 17058, Korea

Energies, 2020, vol. 13, issue 8, 1-16

Abstract: 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.

Keywords: fault diagnosis; gas-insulated switchgear (GIS); long short-term memory (LSTM); partial discharges (PDs); self-attention (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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