A Novel Attention Temporal Convolutional Network for Transmission Line Fault Diagnosis via Comprehensive Feature Extraction

Guangxun E, He Gao (), Youfu Lu, Xuehan Zheng, Xiaoying Ding and Yuanhao Yang
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Guangxun E: Shandong Hi-Speed Group Co., Ltd., Shandong Hi-Speed Mansion, No. 8 Long’ao North Road, Lixia District, Jinan 250098, China
He Gao: Shandong Key Laboratory of Intelligent Buildings Technology, School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China
Youfu Lu: Shandong Hi-Speed Group Co., Ltd., Shandong Hi-Speed Mansion, No. 8 Long’ao North Road, Lixia District, Jinan 250098, China
Xuehan Zheng: Shandong Key Laboratory of Intelligent Buildings Technology, School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China
Xiaoying Ding: Shandong Zhengchen Technology Co., Ltd., No. 11777, Tourist Road, Jinan Area, China (Shandong) Pilot Free Trade Zone, Jinan 250101, China
Yuanhao Yang: Shandong Zhengchen Technology Co., Ltd., No. 11777, Tourist Road, Jinan Area, China (Shandong) Pilot Free Trade Zone, Jinan 250101, China

Energies, 2023, vol. 16, issue 20, 1-21

Abstract: Traditional transmission line fault diagnosis approaches ignore local structure feature information during feature extraction and cannot concentrate more attention on fault samples, which are difficult to diagnose. To figure out these issues, an enhanced feature extraction-based attention temporal convolutional network (EATCN) is developed to diagnose transmission line faults. The proposed EATCN suggests a new comprehensive feature-preserving (CFP) technique to maintain the global and local structure features of original process data during dimension reduction, where the local structure-preserving technique is incorporated into the principal component analysis model. Furthermore, to diagnose transmission line faults more effectively, a CFP-based attention TCN scheme is constructed to classify the global and local structure features of a fault snapshot dataset. To be specific, to cope with the gradient disappearance problem and improve learning capability, a skip connection attention (SCA) network is developed by incorporating a skip connection structure and two fully connected layers into the existing attention mechanism. By combining the developed SCA network with the conventional TCN’s residual blocks, an EATCN-based diagnosis model is then constructed to dynamically pay attention to various imported global and local structure features. Detailed experiments on the datasets of the simulated power system are performed to test the effectiveness of the developed EATCN-based fault diagnosis scheme.

Keywords: fault diagnosis; transmission line; comprehensive feature extraction; attention mechanism; temporal convolutional network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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