Topology identification of low-voltage station based on robust total least squares method

Xiaoyan Yuan; Huafeng Cao; Kun Wang

doi:10.1117/12.2692835

24 October 2023 Topology identification of low-voltage station based on robust total least squares method

Xiaoyan Yuan, Huafeng Cao, Kun Wang

Proceedings Volume 12804, Second International Conference on Sustainable Technology and Management (ICSTM 2023); 1280417 (2023) https://doi.org/10.1117/12.2692835
Event: 2nd International Conference on Sustainable Technology and Management (ICSTM2023), 2023, Dongguan, China

Abstract

The low-voltage station area has a long-standing problem of line topology confusion, which leads to difficulties in locating faults in the distribution network and making maintenance difficult. In order to accurately identify the topological relationships of low-voltage stations, we proposes a low-voltage station topology identification algorithm based on the robust total least squares. We use the least squares method as the theoretical basis. Anti-variance estimation is added to the algorithm to further improve its ability to suppress coarse variances and thus improve recognition accuracy. The algorithm collects the active current values of customer meters in the station area at multiple time points. These data are used as the basis for establishing matrix equations to identify the topological relationships in the station area. Finally, the algorithm is compared with the least squares and weighted least squares to verify the accuracy of the algorithm. The results show that the Robust Total Least Squares has high accuracy for the topological identification of low-voltage station and is suitable for the application requirements of low-voltage station area.

Citation Download Citation

Xiaoyan Yuan, Huafeng Cao, and Kun Wang "Topology identification of low-voltage station based on robust total least squares method", Proc. SPIE 12804, Second International Conference on Sustainable Technology and Management (ICSTM 2023), 1280417 (24 October 2023); https://doi.org/10.1117/12.2692835

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