Research on fault detection of critical transmission lines based on multi-source data fusion algorithm

Qi Wang; Jianing Shang; Wenjian Zheng; An Chang; Mandi Cui

doi:10.1117/12.2672910

25 April 2023 Research on fault detection of critical transmission lines based on multi-source data fusion algorithm

Qi Wang, Jianing Shang, Wenjian Zheng, An Chang, Mandi Cui

Proceedings Volume 12598, Eighth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2022); 125983A (2023) https://doi.org/10.1117/12.2672910
Event: Eighth International Conference on Energy Materials and Electrical Engineering (ICMEE 2022), 2022, Guangzhou, China

Abstract

Key transmission line fault detection methods have the problem of large detection errors and design a key transmission line fault detection method based on multi-source data fusion algorithm. The component symmetry between the forward and inverse traveling waves is used to discriminate the traveling wave abnormal region, combine the time window, pinpoint the information of the time field to the daily shift data, construct a tripping warning model based on the multi-source data fusion algorithm, and optimize the critical transmission line fault detection mode. Experimental results show that the mean error values of the key transmission line fault detection method in the paper, compared with three other key transmission line fault detection methods, are: 0.059, 0.113, 0.120 and 0.115 respectively, proving that the use of the fault detection method is improved when combined with the multi-source data fusion algorithm.

Citation Download Citation

Qi Wang, Jianing Shang, Wenjian Zheng, An Chang, and Mandi Cui "Research on fault detection of critical transmission lines based on multi-source data fusion algorithm", Proc. SPIE 12598, Eighth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2022), 125983A (25 April 2023); https://doi.org/10.1117/12.2672910

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KEYWORDS

Data transmission

Data fusion

Detection and tracking algorithms

Lightning

Data modeling

Deep learning

Dielectrics

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