Channel transmission fault detection method for smart grid based on multivariate data analysis

Weifeng Luo Sr.

doi:10.1117/12.2611346

14 October 2021 Channel transmission fault detection method for smart grid based on multivariate data analysis

Weifeng Luo Sr.

Proceedings Volume 11930, International Conference on Mechanical Engineering, Measurement Control, and Instrumentation; 119301Q (2021) https://doi.org/10.1117/12.2611346
Event: International Conference on Mechanical Engineering, Measurement Control, and Instrumentation (MEMCI 2021), 2021, Guangzhou, China

Abstract

Based on the analysis of the channel characteristics of transmission lines in distribution network, the channel characteristics of different types of transmission lines are analyzed and compared, and the channel attenuation model of transmission lines is established. Aiming at the multi-path fault of smart grid transmission channel, a multi-path fault detection method is proposed. Through the establishment of multi-channel model, the transmission characteristics of power line are studied and analyzed by using nonlinear least square method and multi-channel parameter estimation method. The power line fault location under high resistance condition is analyzed, and the relationship between the parameters and the power line fault is obtained. On this basis, the topology of power equipment network structure is identified, and the subspace method is compared with the matrix beam method. Based on the root adjacency relationship, the network topology is reconstructed by dynamic tree reconstruction. In the process of smart grid channel transmission, multivariate data analysis method is used for fault detection, and it is verified by experiments. The results show that the channel transmission fault detection method of smart grid based on multivariate data analysis has high calculation accuracy and can meet the research requirements.

Citation Download Citation

Weifeng Luo Sr. "Channel transmission fault detection method for smart grid based on multivariate data analysis", Proc. SPIE 11930, International Conference on Mechanical Engineering, Measurement Control, and Instrumentation, 119301Q (14 October 2021); https://doi.org/10.1117/12.2611346

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