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

Energies

Yong-Hwa Kim¹  Viet-Hung Nguyen¹  Minh-Tuan Nguyen¹  Suk-Jun Yun² 

[1] Department of Electronic Engineering, Myongji University, Yongin 449-728, Korea;Prevention Diagnosis Team, Genad System, Naju 58296, Korea;

关键词: fault diagnosis;    gas-insulated switchgear (GIS);    long short-term memory (LSTM);    partial discharges;    recurrent neural network (RNN);   

DOI  :  10.3390/en11051202

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown