Year: 2022
Type: conference-paper
Abstract: Different types of partial discharge (PD) cause different damage to gas-insulated switchgear (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. This work proposed a Convolutional Block Attention Module Residual Network (CBAM-ResNet) for the GIS PD pattern recognition. In particular, the Squeeze-and-Excitation mechanism was used to improve the recognition accuracy. The GIS phase resolved partial discharge (PRPD) image is used as the input of the pattern recognition model. The results show that the recognition accuracy of the proposed method is as high as 89%, and has a recognition accuracy of 92.72%, 90.19%, 89.36%, and 93.62% for the four PD types, respectively. Compared with normal CNN and SVM recognition algorithms, the PD pattern recognition accuracy of the proposed method has been greatly improved. And the CBAM-ResNet for PD pattern recognition has a good application prospect in engineering practice.
Authors: Di Hu, Zhong Chen, Wei Yang, Taiyun Zhu, Yanguo Ke
Institution: State Grid Corporation of China (China)
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4366380909