文章基本信息

标题：Intelligent Identification of the Line-Transformer Relationship in Distribution Networks Based on GAN Processing Unbalanced Data
本地全文：下载
作者：Wang, Yan ; Zhang, Xinyu ; Liu, Haofeng 等
期刊名称：Sustainability
印刷版ISSN：2071-1050
出版年度：2022
卷号：14
期号：14
页码：1-15
DOI：10.3390/su14148611
语种：English
出版社：MDPI, Open Access Journal
摘要：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.
关键词：line-transformer relationship; unbalanced data; feature extraction; generative adversarial networks