现代电力2025，Vol.42Issue(1)：1-11,11.DOI:10.19725/j.cnki.1007-2322.2022.0464
基于电气特征和地理相关性的配电网线变关系辨识
Identification of Feeder-transformer Connectivity in Distribution Network Based on Electrical Characteristics and Geographic Relevance
摘要
Abstract
The accurate feeder-transformer connectivity of dis-tribution network serves as a crucial data basis for power grid operations,such as loss calculation,operation and maintenance.The manual investigation method,however,incurs high cost and exhibits low efficiency,making it unsuitable for its applica-tion in the distribution network characterized by increasingly complex network structure and frequent changes.In view of this,a method to identify the feeder-transformer connectivity is proposed based on the measurement data and the equipment archives information.Firstly,the transformers that are electric-ally close to each other are merged based on the principle of voltage similarity,and then a virtual transformer is constructed as the subsequent input.After that,the evaluation indexes for feeder-transformer energy difference and voltage similarity are constructed respectively,followed by the establishment of an optimal identification model for feeder-transformer connectiv-ity based on electrical characteristic indexation.Finally,to re-duce the impact of measurement data quality on the model,the geographical location information of distribution transformer is added to the original model.Consequently,an improved optim-al identification model considering geographical correlation is obtained.It has been verified in the actual distribution system that this method demonstrates significant advantages in improv-ing the identification accuracy of line transformer relationship,and holds certain practical engineering application value as well as a guiding role.关键词
配电网/线变关系/电压相似性/能量差异/地理相关性/联合优化Key words
distribution network/feeder-transformer con-nectivity/voltage similarity/energy difference/geographic-al correlation/joint optimization分类
动力与电气工程引用本文复制引用
李岸滕,赵健,宣羿,孙智卿..基于电气特征和地理相关性的配电网线变关系辨识[J].现代电力,2025,42(1):1-11,11.基金项目
国家自然科学基金项目(51907114). Project Supported by National Natural Science Foundation of China(51907114). （51907114）
