Open Access iconOpen Access

ARTICLE

crossmark

Identification Method for Users-Transformer Relationship in Station Area Based on Local Selective Combination in Parallel Outlier Ensembles Algorithm

Yunlong Ma1, Junwei Niu2,*, Bo Xu3, Xingtao Song2, Wei Huang2, Guoqiang Sun2

1 State Grid Jiangsu Electric Power Co., Ltd., Nanjing, 210000, China
2 College of Energy and Electrical Engineering, Hohai University, Nanjing, 210000, China
3 Jiangsu Frontier Electric Power Technology Co., Ltd., Nanjing, 210000, China

* Corresponding Author: Junwei Niu. Email: email

Energy Engineering 2023, 120(3), 681-700. https://doi.org/10.32604/ee.2023.024719

Abstract

In the power distribution system, the missing or incorrect file of users-transformer relationship (UTR) in low-voltage station area (LVSA) will affect the lean management of the LVSA, and the operation and maintenance of the distribution network. To effectively improve the lean management of LVSA, the paper proposes an identification method for the UTR based on Local Selective Combination in Parallel Outlier Ensembles algorithm (LSCP). Firstly, the voltage data is reconstructed based on the information entropy to highlight the differences in between. Then, the LSCP algorithm combines four base outlier detection algorithms, namely Isolation Forest (I-Forest), One-Class Support Vector Machine (OC-SVM), Copula-Based Outlier Detection (COPOD) and Local Outlier Factor (LOF), to construct the identification model of UTR. This model can accurately detect users’ differences in voltage data, and identify users with wrong UTR. Meanwhile, the key input parameter of the LSCP algorithm is determined automatically through the line loss rate, and the influence of artificial settings on recognition accuracy can be reduced. Finally, this method is verified in the actual LVSA where the recall and precision rates are 100% compared with other methods. Furthermore, the applicability to the LVSAs with difficult data acquisition and the voltage data error in transmission are analyzed. The proposed method adopts the ensemble learning framework and does not need to set the detection threshold manually. And it is applicable to the LVSAs with difficult data acquisition and high voltage similarity, which improves the stability and accuracy of UTR identification in LVSA.

Keywords


Cite This Article

APA Style
Ma, Y., Niu, J., Xu, B., Song, X., Huang, W. et al. (2023). Identification method for users-transformer relationship in station area based on local selective combination in parallel outlier ensembles algorithm. Energy Engineering, 120(3), 681-700. https://doi.org/10.32604/ee.2023.024719
Vancouver Style
Ma Y, Niu J, Xu B, Song X, Huang W, Sun G. Identification method for users-transformer relationship in station area based on local selective combination in parallel outlier ensembles algorithm. Energ Eng. 2023;120(3):681-700 https://doi.org/10.32604/ee.2023.024719
IEEE Style
Y. Ma, J. Niu, B. Xu, X. Song, W. Huang, and G. Sun "Identification Method for Users-Transformer Relationship in Station Area Based on Local Selective Combination in Parallel Outlier Ensembles Algorithm," Energ. Eng., vol. 120, no. 3, pp. 681-700. 2023. https://doi.org/10.32604/ee.2023.024719



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 767

    View

  • 433

    Download

  • 0

    Like

Share Link