Identification of distribution network topology parameters based on multidimensional operation data

Li, Jiaqiao; Wu, Di; Wang, Jin; Chu, Zhenyue; Liu, Shengyuan; Ma, Jien; Lin, Zhenzhi; Yang, Li

doi:10.1016/j.egyr.2021.01.065

Cited by 8 publications

(1 citation statement)

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“…Authors [6,7] determine the similarity of the user voltage curve according to the Pearson correlation coefficient, check the line to which the transformer belongs, and identify the transformer that does not belong to the line. Author [8] uses the correlation coefficient between user voltage sequence data as the correlation measure in the outlier algorithm to calculate the degree of outliers in the user ring domain, so as to verify the users with the wrong line-transformer relationship. Although these methods based on the correlation analysis of voltage curves can solve the problem of inaccurate line-transformer relationship to a certain extent, there are still some limitations.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Identification of the Line-Transformer Relationship in Distribution Networks Based on GAN Processing Unbalanced Data

et al. 2022

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The wrong line-transformer relationship is one of the main reasons that leads to the failure of the line loss assessment of the distribution network with voltage levels of 10 kV and below. The traditional manual method to verify the line-transformer relationship is time-consuming, labor-intensive and inefficient. At the same time, due to the small sample size of the data with abnormal line-transformer relationship, the unbalanced sample data reduces the accuracy of the artificial intelligence algorithm. To this end, this paper proposes an intelligent identification method for distribution network line-transformer relationship based on Generative Adversarial Networks (GAN) processing unbalanced data. Firstly, perform data preprocessing and feature extraction based on the input power of the distribution line and the power consumption of each distribution transformer; then, build a GAN-based model for expanding the data of only a small number of abnormal line-transformer relationship samples, so as to solve the problem of unbalanced sample data distribution; and finally, establish a support vector machine (SVM) to realize the classification of the line-transformer relationship. The results of the example simulation show that, compared with the traditional Synthetic Minority Oversampling Technique (SMOTE) for processing unbalanced data, the classification effect of the proposed GAN-based data augmentation method has been significantly improved. In addition, the recall rate of the three types of the line-transformer relationship (line hanging error, magnification error and normal) under the line-transformer relationship identification method proposed in this paper is more than 92%, which proves the effectiveness and feasibility of the method.

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Section: Introductionmentioning

confidence: 99%