| 引用本文: | 张江林,张亚超,洪居华,高红均,刘俊勇.基于离散小波变换和模糊K-modes的负荷聚类算法[J].电力自动化设备,2019,39(2): |
| ZHANG Jianglin,ZHANG Yachao,HONG Juhua,GAO Hongjun,LIU Junyong.A load clustering algorithm based on discrete wavelet transform and fuzzy K-modes[J].Electric Power Automation Equipment,2019,39(2): |
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| 摘要: |
| 为了研究智能电网背景下用户的用电模式，考虑到现有聚类算法的不足，提出了一种基于离散小波变换的模糊K-modes聚类算法。利用离散小波变换将时域的负荷曲线转换到频域，从而将负荷曲线的不同特征隔离在不同的频域水平，并利用低阶近似的思想选取原始曲线的有效分量曲线；对所选的分量曲线进行趋势编码，将连续负荷数据转化为离散类属性数据；基于平均密度确定初始聚类条件，利用模糊K-modes聚类算法对曲线进行形态聚类，得到负荷曲线模板；将所提算法与传统K-means算法及层次聚类算法进行比较，从而验证了所提算法的有效性。 |
| 关键词: 智能电网 负荷聚类 离散小波变换 模糊K-modes聚类算法 用电模式 |
| DOI：10.16081/j.issn.1006-6047.2019.02.015 |
| 分类号:TM761 |
| 基金项目:国家自然科学基金重点项目(5143000228)；中央高校基本科研业务费专项资金资助项目(YJ201750) |
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| A load clustering algorithm based on discrete wavelet transform and fuzzy K-modes |
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ZHANG Jianglin1,2, ZHANG Yachao3, HONG Juhua1, GAO Hongjun1, LIU Junyong1
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1.College of Electrical Engineering and Information Technology, Sichuan University, Chengdu 610065, China;2.School of Control Engineering, Chengdu University of Information Technology, Chengdu 610225, China;3.State Grid Chongqing Qinan Power Supply Company, Chongqing 401420, China
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| Abstract: |
| In order to study the power consumption modes of users under the background of smart grid, a fuzzy K-modes clustering algorithm based on discrete wavelet transform is proposed considering the deficiencies of existing clustering algorithms. The load curves in the time domain are converted to the frequency domain by the discrete wavelet transform, so that the different features of load curve can be isolated at different frequency domain levels. The effective component curves of the primitive curve are selected by the idea of lower order approximation. The selected component curves are coded and the continuous load data are translated into discrete attribute data. The initial clustering condition is determined based on average density and the shapes of curves are clustered by the fuzzy K-modes clustering algorithm, based on which, the load curve forms are obtained. The effectiveness of the proposed algorithm is verified by comparing it with the traditional K-means algorithm and the hierarchical clustering algorithm. |
| Key words: smart grid load clustering discrete wavelet transform fuzzy K-modes clustering algorithm power consumption mode |
