|本期目录/Table of Contents|

[1]张艳霞,尹佳鑫,蒙高鹏,等. 一种基于广域测量信息的在线同调分群方法[J].电机与控制学报,2019,23(05):10-17.[doi:10.15938/j.emc.2019.05.002]
 ZHANG Yan-xia,YIN Jia-xin,MENG Gao-peng,et al. Method of online recognition of coherent generators based on wide area information[J].,2019,23(05):10-17.[doi:10.15938/j.emc.2019.05.002]
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 一种基于广域测量信息的在线同调分群方法
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《电机与控制学报》[ISSN:1007-449X/CN:23-1 408/TM]

卷:
23
期数:
2019年05
页码:
10-17
栏目:
出版日期:
2019-05-15

文章信息/Info

Title:
 Method of online recognition of coherent generators based on wide area information
作者:
 张艳霞 尹佳鑫 蒙高鹏 李杰 李多多
 ( 天津大学智能电网教育部重点实验室,天津300072)
Author(s):
 ZHANG Yan-xia YIN Jia-xin MENG Gao-peng LI Jie LI Duo-duo
 ( Key Laboratory of Smart Grid of Ministry of Education,Tianjin University,Tianjin 300072,China)
关键词:
 广域测量系统 同调分群 功角差有效值 相关系数 Hsim 函数 属性阈值聚类 k-means聚类
Keywords:
wide area measurement system coherency identification effective values of the power angle difference correlation coefficient Hsim function quality threshold clustering k-means clustering
分类号:
TM 721
DOI:
10.15938/j.emc.2019.05.002
文献标志码:
A
摘要:
为了能够快速准确地识别同调机群,提出两种基于广域测量信息的同调分群指标。在此基础上,首先利用发电机间的功角差有效值指标快速地实现预分群; 然后利用皮尔森相关系数和
Hsim 函数相结合构造新的相似度度量指标ρHsim,既考虑发电机功角曲线间的距离差异又考虑走势差异,实现了更严格的再分群。为了实现再分群的计算,采用属性阈值( QT) 聚类和k-means 聚类相结合的改进聚类算法。EPRI-36 系统仿真计算的结果表明,在预分群阶段应用发电机功角差有效值指标可以迅速地对功角摆开差异较大的发电机进行分群,再分群阶段应用ρHsim 相似度指标,可以实现各种精度的发电机同调分群。
Abstract:
 In order to identify coherent generator groups quickly and accurately,two clustering indexes based on wide area measurement information are proposed. Firstly,the result of the pre-grouping could be attained by calculating the effective values of the power angle difference among all the generators.Then,a similarity measure method called ρHsim was constructed by combining the Pearson correlation coefficient and the Hsim function. It considered the distance difference and the trend difference of the generator
power angle trajectories at the same time,which can be applied to attain the result of further-grouping.An improved clustering algorithm combining quality threshold ( QT) clustering and k-means clustering was proposed to meet the requirements of online recognition of coherent generators. The effectiveness of the proposed method is verified by the EPRI-36 node system simulation example. Analytical results show that the generators with large power angle difference can be quickly assigned to different groups in
the pre-grouping stage by using the effective values of the power angle difference. In the further-grouping stage,the result of coherency identification at different scales and levels of detail can be attained by using the new similarity measure index ρHsim.

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备注/Memo

备注/Memo:
收稿日期: 2017 - 05 - 05
作者简介: 张艳霞( 1962—) ,女,博士,教授,博士生导师,研究方向为电力系统继电保护;
尹佳鑫( 1992—) ,男,硕士研究生,研究方向为电力系统继电保护;
蒙高鹏( 1990—) ,男,硕士研究生,研究方向为电力系统继电保护;
李杰( 1990—) ,男,硕士研究生,研究方向为电力系统继电保护;
李多多( 1991—) ,男,硕士研究生,研究方向为电力系统继电保护。
通信作者: 张艳霞
更新日期/Last Update: 2019-06-29