摘要
针对传统的基于人工方式的台区低压拓扑关系识别方法存在效率低、成本高,且在拓扑变化时需要重新识别,难以满足实时性要求等问题,提出了一种基于云边融合的台区低压拓扑关系智能识别方法。该方法首先建立了云-边协同的数据应用框架,在此框架基础上采用皮尔逊算法来识别用户单表的相位拓扑关系,并采用K-means算法对用户特征进行聚类分析,最终识别不同台区用户的相位、接入表箱信息等拓扑关系。最后通过实例验证所提智能识别算法的有效性。
Traditional manual-based low-voltage topological relationship identification method in the station area has problems such as low efficiency and high cost,it needs to be re-identified when the topology changes,and it is difficult to meet the real-time requirements.An intelligent identification method of low-voltage topology relationship in station area based on cloud-edge fusion is proposed.Firstly,a data application framework of cloud-edge collaboration is established.On the basis of this framework,Pearson algorithm is used to iden-tify the phase topology relationship of the user’s single table,K-means algorithm is used to cluster and analyze the user characteristics,and finally the different topological relations such as phase and access meter box information of users in the station area are identified.Results of real case experiment verify the effectiveness of the proposed intelligent identification method.
作者
张海峰
李晓华
周兴华
ZHANG Haifeng;LI Xiaohua;ZHOU Xinghua(Power Center of Peking University,Beijing 100871,China;Beijing Guodiantong Network Technology Co.,Ltd.,Beijing 100089,China;Beijing Join Bright Digital Power Technology Co.,Ltd.,Beijing 100085,China)
出处
《电子器件》
CAS
北大核心
2023年第2期542-547,共6页
Chinese Journal of Electron Devices
基金
国家重点研发计划项目(2022YFE0105200)。
关键词
低压台区
拓扑识别
云边融合
皮尔逊算法
K-MEANS算法
low-voltage station area
topology identification
cloud-edge fusion
Pearson algorithm
K-means algorithm
