Recognition of sectional line loss anomaly in low-voltage substations based on multidimensional feature constrained clustering

Kaixuan Chang; Zhiqiang Hu; Fan Liu; Yongchao Luo; Desen Ji

doi:10.1117/12.3003831

11 October 2023 Recognition of sectional line loss anomaly in low-voltage substations based on multidimensional feature constrained clustering

Kaixuan Chang, Zhiqiang Hu, Fan Liu, Yongchao Luo, Desen Ji

Author Affiliations +

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128002U (2023) https://doi.org/10.1117/12.3003831
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

The line loss in low-voltage substation area is difficult to identify and the identification accuracy is low. In order to improve the accuracy of line loss judgment, a method based on multi-dimensional feature constrained clustering is proposed to identify sectional line loss anomalies in low-voltage substations. First of all, use LOF algorithm to collect electricity information, judge abnormal information of line loss data, and eliminate irrelevant data. Secondly, Hasusdorff distance formula is improved to calculate the multi-dimensional feature similarity of line loss data in power grid substation area, and the similarity matrix between line loss data is obtained. Finally, the multidimensional Hasusdorff distance is applied to the hierarchical clustering algorithm to identify the anomalies in the line loss data of power grid substation areas. The experimental results show that the proposed method has a good effect on identifying the line loss of low-voltage stations.

Citation Download Citation

Kaixuan Chang, Zhiqiang Hu, Fan Liu, Yongchao Luo, and Desen Ji "Recognition of sectional line loss anomaly in low-voltage substations based on multidimensional feature constrained clustering", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128002U (11 October 2023); https://doi.org/10.1117/12.3003831

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