基于离散小波变换和模糊K-modes的负荷聚类算法
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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。
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.
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.
引文
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[2]郝然,艾芊,肖斐.基于多元大数据平台的用电行为分析构架研究[J].电力自动化设备,2017,37(8):20-27.HAO Ran,AI Qian,XIAO Fei. Architecture based on multivariate big data platform for analyzing electricity consumption behavior[J].Electric Power Automation Equipment,2017,37(8):20-27.
[3]王德文,刘晓建.基于MapReduce的电力设备并行故障诊断方法[J].电力自动化设备,2014,34(10):116-120.WANG Dewen,LIU Xiaojian. Parallel fault diagnosis based on MapReduce for electric power equipments[J]. Electric Power Automation Equipment,2014,34(10):116-120.
[4]钟宇峰,黄民翔,文福拴,等.计及绿色证书交易机制的大用户直购电市场均衡分析[J].电力自动化设备,2014,34(2):144-150.ZHONG Yufeng,HUANG Minxiang,WEN Fushuan,et al. Equilibrium analysis of direct electricity purchase with green certificate mechanism[J]. Electric Power Automation Equipment,2014,34(2):144-150.
[5]王星华,陈卓优,彭显刚.一种基于双层聚类分析的负荷形态组合识别方法[J].电网技术,2016,40(5):1495-1501.WANG Xinghua,CHEN Zhuoyou,PENG Xiangang. A new combinational electrical load analysis method based on bilayer clustering analysis[J]. Power System Technology,2016,40(5):1495-1501.
[6]卜凡鹏,陈俊艺,张琪祁,等.一种基于双层迭代聚类分析的负荷模式可控精细化识别方法[J].电网技术,2018,42(3):903-913.BU Fanpeng,CHEN Junyi,ZHANG Qiqi,et al. A controllable refined recognition method of electrical load pattern based on bilayer iterative clustering analysis[J]. Power System Technology,2018,42(3):903-913.
[7]宋易阳,李存斌,祁之强.基于云模型和模糊聚类的电力负荷模式提取方法[J].电网技术,2014,38(12):3378-3383.SONG Yiyang,LI Cunbin,QI Zhiqiang. Extraction of power load patterns based on cloud model and fuzzy clustering[J]. Power System Technology,2014,38(12):3378-3383.
[8]林顺富,田二伟,符杨,等.基于信息熵分段聚合近似和谱聚类的负荷分类方法[J].中国电机工程学报,2017,37(8):2242-2253.LIN Shunfu,TIAN Erwei,FU Yang,et al. Power load classification method based on information entropy piecewise aggregate approximation and spectral clustering[J]. Proceedings of the CSEE,2017,37(8):2242-2253.
[9]CHEN Z,HENG S,RAN L,et al. Demand side response performance assessment:an impact analysis of load profile accuracy on DSR performances[C]∥2015 IEEE Power Energy Soc. Gen. Meeting.[S.l.]:IEEE,2015:1-5.
[10] GERBEC D,GASPERIC S,SMON I,et al. Allocation of the load profiles to consumers using probabilistic neural networks[J]. IEEE Transactions on Power Systems,2005,20(2):548-555.
[11]COSTA F B. Boundary wavelet coefficients for real-time detection of transients induced by faults and power-quality disturbances[J].IEEE Transactions on Power Delivery,2014,29(6):2674-2687.
[12]PANDEY A S,SINGH D,SINHA S K. Intelligent hybrid wavelet models for short-term load forecasting[J]. IEEE Transactions on Power Systems,2010,25(3):1266-1273.
[13]HAMID E Y,KAWASAKI Z I. Wavelet-based data compression of power system disturbances using the minimum description length criterion[J]. IEEE Transactions on Power Delivery,2002,17(2):460-466.
[14]曹文婷,邹海,段凤玲.基于模糊K-Modes和免疫遗传算法的聚类分析[J].计算机技术与发展,2009,19(2):151-153.CAO Wenting,ZOU Hai,DUAN Fengling. Cluster analysis based on fuzzy K-Modes and immune genetic algorithm[J]. Computer Technology and Development,2009,19(2):151-153.
[15]白亮,曹付元,梁吉业.基于新的相异度量的模糊K-Modes聚类算法[J].计算机工程,2009,35(16):192-194.BAI Liang,CAO Fuyuan,LIANG Jiye. Fuzzy K-Modes clustering algorithm based on new dissimilarity measure[J]. Computer Engineering,2009,35(16):192-194.
[16]LI R,LI F R,SMITH N D. Load characterization and low-order approximation for smart metering data in the spectral domain[J].IEEE Transactions on Industrial Informatics,2017,13(3):976-984.
[17]ROCHAREIS A J,ALVESDASILVA A P. Feature extraction via multiresolution analysis for short-term load forecasting[J]. IEEE Transactions on Power Systems,2005,20(1):189-198.
[18]CAO F Y,LIANG J Y,BAI L. A new initialization method for categorical data clustering[J]. Expert Systems with Applications,2009,36(7):10223-10228.
[19]IMAMURA K,KUBO N,HASHIMOTO H. Automatic moving object extraction using x-means clustering[C]∥28th Picture Coding Symposium. Nagoya,Japan:IEEE,2010:246-249.
[20]ZHONG S Y,TAM K S. A frequency domain approach to characterize and analyze load profiles[J]. IEEE Transactions on Power Systems,2012,27(2):857-865.
[21]MANDAL P,SENJYU T,URASAKI N,et al. A novel approach to forecast electricity price for PJM using neural network and similar days method[J]. IEEE Transactions on Power Systems,2007,22(4):2058-2065.
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