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

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) recurren...

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Bibliographic Details
Published in:Energies (Basel) 2018-05, Vol.11 (5), p.1202
Main Authors: Nguyen, Minh-Tuan, Nguyen, Viet-Hung, Yun, Suk-Jun, Kim, Yong-Hwa
Format: Article
Language:English
Subjects:
Diagnostic software
Diagnostic systems
fault diagnosis
gas-insulated switchgear (GIS)
Levels
Long short-term memory
long short-term memory (LSTM)
Neural networks
partial discharges
recurrent neural network (RNN)
Recurrent neural networks
Support vector machines
Switchgear
Switching theory
Temporal variations
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en11051202