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20 October 2023 Fault diagnosis method for intelligent substation secondary system communication network based on improved TCN
Chunbo Li, Hong Yin, Shourui Liu, Linkang Zhang, Qing Ma, Qihang Wang
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Proceedings Volume 12814, Third International Conference on Green Communication, Network, and Internet of Things (CNIoT 2023); 128140T (2023) https://doi.org/10.1117/12.3011029
Event: Third International Conference on Green Communication, Network, and Internet of Things (CNIoT 2023), 2023, Chongqing, China
Abstract
Aiming at the low accuracy of the Fault Diagnosis (FuDg) methods used in the Intelligent Substation Secondary System Communication Network (ISSSCN), a FuDg method of the ISSSCN based on the improved Time Convolution Network (TCN) is proposed. Firstly, the overall structure of the ISSSCN and the mathematical description method of the fault signal are analyzed. Secondly, a basic model for f FuDg of the ISSSCN is built based on the TCN including the Extended Causal Convolution (ECC) and the ResNet. Finally, the TCN model is optimized by the improved Hybrid Attention Mechanism (H-AM) based on channel and spatial attention, which greatly improves the accuracy of ISSSCN FuDg. The experiment shows that the accuracy of the proposed method for four different types of FuDg reaches 95.72%, 96.83%, 95.32% and 95.28% respectively, which is higher than the other two comparison methods. Therefore, the proposed method has good FuDg ability
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Chunbo Li, Hong Yin, Shourui Liu, Linkang Zhang, Qing Ma, and Qihang Wang "Fault diagnosis method for intelligent substation secondary system communication network based on improved TCN", Proc. SPIE 12814, Third International Conference on Green Communication, Network, and Internet of Things (CNIoT 2023), 128140T (20 October 2023); https://doi.org/10.1117/12.3011029
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KEYWORDS
Convolution
Deep learning
Telecommunications
Intelligence systems
Data modeling
Education and training
Signal processing
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