基于随机矩阵理论和聚类算法的电能表运行状态评估方法

doi:10.11930/j.issn.1004-9649.201907096

中国电力 ›› 2020, Vol. 53 ›› Issue (11): 116-125.DOI: 10.11930/j.issn.1004-9649.201907096

基于随机矩阵理论和聚类算法的电能表运行状态评估方法

程瑛颖, 杜杰, 周全, 张家铭, 张晓勇, 李刚

国网重庆市电力公司电力科学研究院，重庆 401121

收稿日期:2019-07-10 修回日期:2019-10-30 出版日期:2020-11-05 发布日期:2020-11-05
通讯作者: 国家电网公司科技项目(人工智能技术在用电信息采集系统中的应用研究，52200019000C)
作者简介:程瑛颖(1976—)，女，硕士，高级工程师(教授级)，从事电能计量、试验检测及智能量测等研究，E-mail:cyy_99@sina.com;杜杰(1987—)，女，硕士，工程师，从事电能计量、数字化计量、智能电网等研究，E-mail:dujie@cq.sgcc.com.cn
基金资助:
This work is supported by Science and Technology Project of SGCC (No.52200019000C)

Evaluation Method for Running State of Electricity Meters Based on Random Matrix Theory and Clustering Algorithm

CHENG Yingying, DU Jie, ZHOU Quan, ZHANG Jiaming, ZHANG Xiaoyong, LI Gang

State Grid Chongqing Electric Power Research Institute, Chongqing 401121, China

Received:2019-07-10 Revised:2019-10-30 Online:2020-11-05 Published:2020-11-05

摘要/Abstract

摘要： 随着智能配电网络规模的扩大以及电网结构的复杂化，电力大数据呈指数级增长，电力设备的检、监测评估面临新的挑战。在大数据原理和数据挖掘分析的基础上，提出一种基于随机矩阵理论和聚类算法的电能表运行状态评估方法。首先，对电力大数据统一预处理，完成时间序列数据表征；然后，采用实时分离窗技术整合时序数据；其次，基于随机矩阵理论，对多维度电能表时间序列数据实时计算、分析统计量时序特征；进一步，采用改进的时间规整聚类算法计算时序数据相似度，从而对随机矩阵统计量聚类分级；最后，分析聚类结果，得到电能表运行状态评估等级和范围，完成电能表实时运行状态评估。实例分析和对比研究结果表明，与传统的主元分析评估方法相比，所提出的新型电能表运行状态评估方法具有良好的鲁棒性、可靠性和时效性，为电力电网检测技术应用研究提供了新思路。

关键词: 随机矩阵理论, 聚类算法, 移动分离窗, 时间序列, 电力系统大数据, 电能表运行状态

Abstract: With the expansion of intelligent distribution network and the increasing complexity of power grid structure, the amount of data in power system increases rapidly, and new challenges rise from the checking and monitoring evaluation of power equipment. Based on the principle of big data mining analysis, this paper proposes a method based on random matrix theory and clustering algorithm to evaluate the running state of electric energy meter. Firstly, time series data of various indicators are characterized and then integrated by real-time separation window technology. Based on the random matrix theory, the random matrix-based analysis model is constructed to calculate and analyze the characteristics with multi-dimensional statistical timing in real time. Further, an improved DTW (dynamic time warping) clustering algorithm is used to analysis the linear feature statistics of the output of the random matrix. Finally, according to the clustering result, the state of the electric energy meter is obtained and outputted as different classes. The experiments show that compared with the traditional Principal Component Analysis evaluation method, the proposed method has good robustness, reliability and timeliness, which provides a new idea for the application research of power grid detection technology.

Key words: random matrix theory, clustering algorithm, moving-split window, time sequence data, power system big data, running state of electric energy meter

程瑛颖, 杜杰, 周全, 张家铭, 张晓勇, 李刚. 基于随机矩阵理论和聚类算法的电能表运行状态评估方法[J]. 中国电力, 2020, 53(11): 116-125.

CHENG Yingying, DU Jie, ZHOU Quan, ZHANG Jiaming, ZHANG Xiaoyong, LI Gang. Evaluation Method for Running State of Electricity Meters Based on Random Matrix Theory and Clustering Algorithm[J]. Electric Power, 2020, 53(11): 116-125.

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https://www.electricpower.com.cn/CN/Y2020/V53/I11/116

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基于随机矩阵理论和聚类算法的电能表运行状态评估方法

Evaluation Method for Running State of Electricity Meters Based on Random Matrix Theory and Clustering Algorithm

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基于随机矩阵理论和聚类算法的电能表运行状态评估方法

Evaluation Method for Running State of Electricity Meters Based on Random Matrix Theory and Clustering Algorithm

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