| 一种变电站负荷行业构成比例估算方法 |
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| 引用本文： | 施佳君,秦川,鞠平,徐春雷,赵静波,陈彦翔. 一种变电站负荷行业构成比例估算方法[J]. 电力自动化设备, 2019, 39(10) |
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| 作者姓名： | 施佳君 秦川 鞠平 徐春雷 赵静波 陈彦翔 |
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| 作者单位： | 河海大学 能源与电气学院，江苏 南京 211100,河海大学 能源与电气学院，江苏 南京 211100,河海大学 能源与电气学院，江苏 南京 211100,国网江苏省电力有限公司，江苏 南京 210029,国网江苏省电力有限公司电力科学研究院，江苏 南京 211103,河海大学 能源与电气学院，江苏 南京 211100 |
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| 基金项目： | 国家自然科学基金资助项目(51837004);111引智计划“新能源发电与智能电网学科创新引智基地”项目(B14022)；国网江苏省电力有限公司科技项目(J2018042) |
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| 摘 要： | 为解决负荷模型的时变性问题，提高电网运行的安全性和经济性，提出基于能量管理系统底层负荷出线和专线用户的日负荷数据估算变电站负荷行业构成比例的方法。以实际电网数据为例，首先采用因子分析法对高维度的日采样数据进行降维；然后根据降维结果进行K-means聚类，并基于聚类结果进行负荷特性分析，从而获取典型行业日负荷曲线；最后根据所有底层出线和专线用户的行业归属情况及功率，自下而上聚合得到220 kV变电站的负荷行业构成比例。与用电信息采集系统用户数据的比对结果表明，所提方法估算得到的上层变电站负荷行业构成比例与实际总体相符。
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| 关 键 词： | 因子分析；K-means聚类；负荷行业构成比例；时变性；负荷建模 |
Estimation method of industry composition of substation loads |
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| SHI Jiajun,QIN Chuan,JU Ping,XU Chunlei,ZHAO Jingbo and CHEN Yanxiang. Estimation method of industry composition of substation loads[J]. Electric Power Automation Equipment, 2019, 39(10) |
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| Authors: | SHI Jiajun QIN Chuan JU Ping XU Chunlei ZHAO Jingbo CHEN Yanxiang |
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| Affiliation: | College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China,College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China,College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China,State Grid Jiangsu Electric Power Co.,Ltd.,Nanjing 210029, China,Electric Power Research Institute of State Grid Jiangsu Electric Power Co.,Ltd.,Nanjing 211103, China and College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China |
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| Abstract: | In order to solve the time variation problem of load model and improve the operation security and economy of power grid, an estimation method of industry composition of substation loads is proposed based on the daily load data of bottom load feeders and special line users acquired by energy management system. The data of an actual power grid is taken as an example. Firstly, the factor analysis method is used to reduce the dimension of high-dimension daily sampling data. Then, K-means clustering is carried out according to the dimension reduction results, and the load characteristics are analyzed based on the clustering results to obtain the daily load curves of typical industries. Finally, according to the categories and powers of all the bottom load feeders and special line users, the industry composition of 220 kV substation loads is obtained by bottom-up aggregation. The comparison results with the user data from AMI(Advanced Metering Infrastructure) show that the industry composition of upper substation loads estimated by the proposed method is correspond to the actual situation. |
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| Keywords: | factor analysis K-means clustering industry composition of loads time variation load modeling |
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