引入改进模糊C均值聚类的负荷数据辨识及修复方法

KONG Xiangyu

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引入改进模糊C均值聚类的负荷数据辨识及修复方法

更新时间：2025-08-13

- 引入改进模糊C均值聚类的负荷数据辨识及修复方法
- Automation of Electric Power Systems Vol. 41, Issue 9, Pages: 90-95(2017)
- 作者机构：
  
  智能电网教育部重点实验室(天津大学),天津市,300072
- 作者简介：
- 基金信息：
- DOI：
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- Published：2017
- 稿件说明：
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KONG Xiangyu. [J]. Automation of electric power systems, 2017, 41(9): 90-95.
DOI：

KONG Xiangyu. [J]. Automation of electric power systems, 2017, 41(9): 90-95. DOI：

摘要

高级量测体系的建设促使大量用电负荷数据增加了可观性，但由于通信等原因，量测数据中存在不良数据。文中提出一种引入改进模糊C均值(FCM)聚类算法的负荷数据辨识及修复方法，该方法利用快速爬山技术，对标准FCM聚类算法中聚类数目难以预先确定、初始聚类中心随机选取等缺点进行改进，实现用电负荷数据的精准聚类。在此基础上提取可行域矩阵及特征曲线，实现对新量测数据的辨识及修正。最后采用某地实际负荷测量数据进行分析，并通过与基于标准FCM聚类算法的对比，验证了该方法的快速性、高效性及其应用前景。

Abstract

With the development of advanced metering infrastructure(AMI)

the observability of power utility information is promoted

but lots of bad data exist in the vast amounts of measurement data due to communication and other reasons. A load data identification and correction method with an improved fuzzy C-means(FCM)clustering algorithm is proposed. With the rapid mountaineering technique

the proposed method has overcome the shortcomings of the existing standard FCM clustering algorithm

which is unable to determine the number of clusters in advance and the random choice of the initial cluster centers. Based on the load data clustering obtained

the feasible region matrix and the load characteristic curve can be calculated for identification and correction of the new load data. Finally

a case of actual load measuring data is analyzed and compared with similar cases treated by the standard FCM clustering algorithm

the proposed method proved fast and efficient.

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Load Curve Clustering Method Based on Euclidean Dynamic Time Warping Distance and Entropy Weight

采用马尔可夫链—多场景技术的交直流主动配电网优化调度

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