电力信息与通信技术2025，Vol.23Issue(11)：51-58,8.DOI:10.16543/j.2095-641x.electric.power.ict.2025.11.07
可自适应聚类的户变识别深度模型研究
Research on Adaptive Clustering-based Deep Model for Customer-transformer Relationship Identification
摘要
Abstract
In low-voltage distribution systems,accurate identification of customer-transformer relationships is crucial for system operation and management.This paper proposes an automatic customer-transformer relationship identification algorithm based on a deep Gaussian mixture model,which leverages noise-labels and semi-supervised learning theory.By designing a clustering main network and a splitting and merging subnetwork structure,it fully utilizes existing label in-formation to achieve document establishment or verification in scenarios where the number of substations is unknown.In experiments on identifying customer-transformer` relationships based on actual user data,the proposed algorithm achieves an accuracy of up to 98.8%in profiling unlabeled users,demonstrating superior identification performance compared to traditional clustering and semi-supervised deep learning algorithms under various operating conditions.关键词
混合高斯/户变关系识别/自适应聚类/低压配电系统Key words
GMM/customer-transformer relationship identification/adaptive clustering/low-voltage distribution system分类
电子信息工程引用本文复制引用
郭祥葛,吕志宁,李毅,郑杰,史万福,王辉..可自适应聚类的户变识别深度模型研究[J].电力信息与通信技术,2025,23(11):51-58,8.基金项目
中国南方电网公司科技项目"基于负荷特征传导的低压全景成图技术研究及应用"(090000KK52222164). （090000KK52222164）
