A GIS Partial Discharge Pattern Recognition Method Based on Improved CBAM-ResNet

Different types of partial discharge (PD) cause different damages to gas-insulated substation (GIS), so it is very important to correctly identify the type of PD for evaluating the GIS insulation condition. The traditional PD pattern recognition algorithm has the limitations of low recognition accur...

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Bibliographic Details
Published in:Journal of electrical and computer engineering 2023-12, Vol.2023, p.1-10
Main Authors: Hu, Di, Chen, Zhong, Yang, Wei, Zhu, Taiyun, Ke, Yanguo, Yin, Kaiyang
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial neural networks
Breakdowns
Classification
Discharge
Efficiency
Modules
Neural networks
Pattern recognition
Substations
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Summary:Different types of partial discharge (PD) cause different damages to gas-insulated substation (GIS), so it is very important to correctly identify the type of PD for evaluating the GIS insulation condition. The traditional PD pattern recognition algorithm has the limitations of low recognition accuracy and slow recognition speed in engineering applications. To effectively diagnose the GIS PD type and safeguard the safe and reliable operation of the distribution network, a GIS PD method based on improved CBAM-ResNet was proposed in this paper. And the improved CBAM-ResNet takes advantage of the residual neural network and attention mechanism. In particular, the channel attention module and the spatial attention module are connected in parallel in the improved CBAM. The experimental results showed that the GIS PD pattern recognition method proposed herein has a recognition rate of 93.58%, 95.00%, 93.55%, and 93.88% against the four PD types. Compared with the traditional PD pattern recognition algorithm, the algorithm has the advantages of a lightweight model and more accurate recognition results, which carry better engineering application values.
ISSN:2090-0147
2090-0155
DOI:10.1155/2023/9948438