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文章摘要
基于智能电能表采集数据的台户关系识别新方法
New identification method of station area recognition based on data acquisition by intelligent meter
Received:July 18, 2019  Revised:July 18, 2019
DOI:10.19753/j.issn1001-1390.2020.23.018
中文关键词: 配电网  台户关系  电网数据采集
英文关键词: distribution  network, station  area identification, electricity  information acquisition
基金项目:国网陕西省电力公司资助项目
Author NameAffiliationE-mail
Song Xiaolin Electric Power Research Institute of State Grid Shaanxi Electric Power Company 13991251596 @163.com 
Huang Luhan Electric Power Research Institute of State Grid Shaanxi Electric Power Company 522259159 @qq.com 
He Yunlong Electric Power Research Institute of State Grid Shaanxi Electric Power Company 232686066@qq.com 
Zhang Yuanfeng Electric Power Research Institute of State Grid Shaanxi Electric Power Company zyf252@126.com 
Chen Lijian Nanjing jiyun information technology company 22790366@qq.com 
Chen Yue* School of Electrical Engineering, Xi’an Jiaotong University chenyue_hbdyu@163.com 
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中文摘要:
      配电网台区关系识别在多个领域有着重要应用,如果台户关系不准确,会对电网线损分析、数据采集成功率、停电区域预判、三相不平衡治理、费控等一系列问题造成影响,这些问题与电力企业和用户的利益紧密相关。文中基于智能电能表采集的数据,提出了一种新的台户识别算法。该算法较传统算法数据计算量至少降低两个量级,能够识别出台户关系中对应的相位关系,且识别准确率更高。在大数据平台上运行该算法计算48个台区实际数据的台户关系,运行结果证明本算法单次计算准确率达到99%以上,累计识别准确率达100%,可以直接投入电网中使用,解决配电网台户关系识别困难的问题。
英文摘要:
      Distribution network station-area relationship identification has important applications in many fields. If the relationship is inaccurate, it will affect a series of problems, such as the success rate of data acquisition, line loss analysis, blackout area prediction, three-phase unbalanced governance, and so on. These problems are closely related to the interests of power enterprises and users. Based on the data collected by smart watt-hour meter, this paper proposes a new algorithm for the identification of desktop. Compared with the traditional algorithm, this algorithm reduces the amount of data calculation by at least two orders of magnitude, and can identify the corresponding phase relationship in the proposed household relationship, and the recognition accuracy is higher. Running on the large data platform, the algorithm calculates the relationship of 48 stations. The results show that the accuracy rate of single calculation is over 99% and the accumulative recognition rate is 100%. The algorithm can be directly used in the power grid to solve the problem of difficult identification of the relationship between stations in the distribution network.
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