Recurrent Neural Network for Partial Discharge Diagnosis in Gas-Insulated Switchgear

Minh-Tuan Nguyen, Viet-Hung Nguyen, Suk-Jun Yun and Yong-Hwa Kim
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Minh-Tuan Nguyen: Department of Electronic Engineering, Myongji University, Yongin 449-728, Korea
Viet-Hung Nguyen: Department of Electronic Engineering, Myongji University, Yongin 449-728, Korea
Suk-Jun Yun: Prevention Diagnosis Team, Genad System, Naju 58296, Korea
Yong-Hwa Kim: Department of Electronic Engineering, Myongji University, Yongin 449-728, Korea

Energies, 2018, vol. 11, issue 5, 1-13

Abstract: The analysis of partial discharge (PD) signals has been identified as a standard diagnostic tool for monitoring the condition of different electrical apparatuses. This study proposes an approach to detecting PD patterns in gas-insulated switchgear (GIS) using a long short-term memory (LSTM) recurrent neural network (RNN). The proposed method uses phase-resolved PD (PRPD) signals as input, extracts low-level features, and finally, classifies faults in GIS. In the proposed method, LSTM networks can learn temporal dependencies directly from PRPD signals. Most existing models use support vector machines (SVMs) and mainly focus on improving feature representation and extraction manually to analyze PRPD signals. However, the proposed model captures important temporal features with the help of its low-level feature extraction capability from raw inputs. It outperforms conventional SVMs and achieves 96.74% classification accuracy for PRPDs in GIS.

Keywords: fault diagnosis; gas-insulated switchgear (GIS); long short-term memory (LSTM); partial discharges; recurrent neural network (RNN) (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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