摘要
错乱的台区档案致使台区线损率分析、配网故障定位、抢修工单下发和三相不平衡分析等一系列高级应用无法有效开展,严重影响了电网公司建设智能电网的进程。为此,提出了一种基于主成分分析法和改进K-means算法的台区用户识别方法。首先利用主成分分析算法对变压器和用户的电压数据进行数据预处理,从而提升算法效率。基于同一台区变压器和用户电压数据的相关性,对用户电压数据主成分进行聚类,从而实现台区用户识别。算例测试表明,台区用户识别方法能准确有效识别用户台区和相别信息,降低人工及硬件成本,对全面指导低压台区运行、维护、抢修、技改和规划等各领域的工作具有重要意义。
Disordered transformer area files result in the fact that a series of advanced applications such as analysis of line loss rate of the transformer area,distribution network fault location,issuance of emergency repair work orders and three-phase imbalance analysis cannot be carried out effectively,thus seriously affecting the process of smart grid construction in power grid companies.In this background,a transformer area user recognition method was proposed on the basis of principal component analysis and improved K-means algorithm.Firstly,voltage data of the transformers and users were preprocessed through the principle component analysis algorithm,so as to raise the efficiency of the algorithm.Furthermore,based on the correlation of voltage data between transformers and users in the same transformer area,user voltage data were clustered to realize recognition of transformer area users.Example tests indicated that the proposed recognition method could accurately and effectively identify the transformer area of the user and related information and reduce labor and hardware cost,and could be of great significance in overall guidance of the operation,maintenance,emergency repair,technical renovation and planning of low-voltage transformer areas.
作者
吴奇
陈相
周昊
朱富明
Wu Qi;Chen Xiang;Zhou Hao;Zhu Fuming(Honghe Power Supply Bureau,Yunnan Power Grid Co.,Ltd.,Honghe Yunnan 661100,China)
出处
《电气自动化》
2020年第5期55-57,共3页
Electrical Automation
关键词
台区区分
相位识别
用电信息采集
改进K-MEANS算法
智能电网
transformer area differentiation
phase recognition
collection of electricity consumption information
improved K-means algorithm
smart grid
