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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Bibliographic Details
Published in:Energies (Basel) 2020-04, Vol.13 (8), p.2102
Main Authors: Tuyet-Doan, Vo-Nguyen, Nguyen, Tien-Tung, Nguyen, Minh-Tuan, Lee, Jong-Ho, Kim, Yong-Hwa
Format: Article
Language:English
Subjects:
Accuracy
Advantages
Classification
Computation
Electrical equipment
Electrodes
fault diagnosis
gas-insulated switchgear (GIS)
Insulation
Long short-term memory
long short-term memory (LSTM)
Methods
Neural networks
Noise
Onsite
Parallel processing
partial discharges (PDs)
Principal components analysis
Recurrent neural networks
self-attention
Sensors
Switchgear
Switching theory
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en13082102