Open Access

ARTICLE

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

Yunlong Ma¹, Junwei Niu^2,*, 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:

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

Received 06 June 2022; Accepted 29 August 2022; Issue published 03 January 2023

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.

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Keywords

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