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
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摘要
提出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。首先以皮尔逊相关系数为相似性度量进行外层形态相似性聚类,然后分别对外层聚类得到的每一类簇以欧式距离为相似性度量进行内层幅度相近聚类。每层都先在给定的阈值约束下迭代聚类,再对迭代收敛得到的聚类簇合并。实际算例分析结果表明:与传统负荷模式识别方法相比,所提方法改善了负荷形态聚类效果,可识别出形态相似但幅度不同的负荷,还能对聚类精细化程度进行控制,提高了聚类准确率。
1 This paper proposes a controllable refined recognition method of electrical load pattern based on bilayer iterative clustering analysis.Firstly,on outer layer,sample data are clustered with Pearson correlation coefficient function as performance evaluation index.Then,on inner layer,each cluster obtained from the outer clustering is clustered with Euclidean distance function as evaluation index.On each layer,iteration is performed through clustering analysis at threshold and the clusters obtained from iterative convergence are combined.Results show that,compared to traditional load pattern recognition of electricity customers,this method improves clustering effect of load curves and identifies electrical loads with different volumes.It can control precision of clustering,thus greatly improving clustering accuracy.
1 This paper proposes a controllable refined recognition method of electrical load pattern based on bilayer iterative clustering analysis.Firstly,on outer layer,sample data are clustered with Pearson correlation coefficient function as performance evaluation index.Then,on inner layer,each cluster obtained from the outer clustering is clustered with Euclidean distance function as evaluation index.On each layer,iteration is performed through clustering analysis at threshold and the clusters obtained from iterative convergence are combined.Results show that,compared to traditional load pattern recognition of electricity customers,this method improves clustering effect of load curves and identifies electrical loads with different volumes.It can control precision of clustering,thus greatly improving clustering accuracy.
引文
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[11]Kim Y I,Ko J M,Choi S H.Methods for generating TLPs(typical load profiles)for smart grid-based energy programs[C]//2011 IEEE Symposium on Computational Intelligence Applications in Smart Grid(CIASG).Paris,France:IEEE,2011:1-6.
[12]张斌,庄池杰,胡军,等.结合降维技术的电力负荷曲线集成聚类算法[J].中国电机工程学报,2015,35(15):3741-3749.Zhang Bin,Zhuang Chijie,Hu Jun,et al.Ensemble clustering algorithm combined with dimension reduction techniques for power load profiles[J].Proceedings of the CSEE,2015,35(15):3741-3749(in Chinese).
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[14]赵岩,李磊,刘俊勇,等.上海电网需求侧负荷模式的组合识别模型[J].电网技术,2010,34(1):145-151.Zhao Yan,Li Lei,Liu Junyong,et al.Combinational recognition model for demand side load profile in shanghai power grid[J].Power System Technology,2010,34(1):145-151(in Chinese).
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[16]贾慧敏,何光宇,方朝雄,等.用于负荷预测的层次聚类和双向夹逼结合的多层次聚类法[J].电网技术,2007,31(23):33-36.Jia Huimin,He Guangyu,Fang Chaoxiong,et al.Load forecasting by multi-hierarchy clustering combining hierarchy clustering with approaching algorithm in two directions[J].Power System Technology,2007,31(23):33-36(in Chinese).
[17]黄宇腾,侯芳,周勤,等.一种面向需求侧管理的用户负荷形态组合分析方法[J].电力系统保护与控制,2013,41(13):20-25.Huang Yuteng,Hou Fang,Zhou Qin,et al.A new combinational electrical load analysis method for demand side management[J].Power System Protection and Control,2013,41(13):20-25(in Chinese).
[18]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(in Chinese).
[19]彭勃,张逸,熊军,等.结合负荷形态指标的电力负荷曲线两步聚类算法[J].电力建设,2016,37(6):96-102.Peng Bo,Zhang Yi,Xiong Jun,et al.A two-step clustering algorithm combined with load shape index for power load curve[J].Electric Power Construction,2016,37(6):96-102(in Chinese).
[20]周世兵,徐振源,唐旭清.新的K-均值算法最佳聚类数确定方法[J].计算机工程与应用,2010,46(16):27-31.Zhou Shibing,Xu Zhenyuan,Tang Xuqing.New method for determining optimal number of clusters in k-means clustering algorithm[J].Computer Engineering and Applications,2010,46(16):27-31(in Chinese).
[21]刘思,李林芝,吴浩,等.基于特性指标降维的日负荷曲线聚类分析[J].电网技术,2016,40(3):797-803.Liu Si,Li Linzhi,Wu Hao,et al.Cluster analysis of daily load curves using load pattern indexes to reduce dimensions[J].Power System Technology,2016,40(3):797-803(in Chinese).
[22]Wilson E.Commercial and residential hourly load profiles for all TMY3 locations in the United States[EB/OL].(2013-07-02)[2017-05-11].http://en.openei.org/datasets/dataset/commercialand-residentialhourly-load-profiles-for-all-tmy3-locations-in-theunited-states.
[2]刘科研,盛万兴,张东霞,等.智能配电网大数据应用需求和场景分析研究[J].中国电机工程学报,2015,35(2):287-293.Liu Keyan,Sheng Wanxing,Zhang Dongxia,et al.Big data application requirements and scenario analysis in smart distribution network[J].Proceedings of the CSEE,2015,35(2):287-293(in Chinese).
[3]田世明,王蓓蓓,张晶.智能电网条件下的需求响应关键技术[J].中国电机工程学报,2014,34(22):3576-3589.Tian Shiming,Wang Beibei,Zhang Jing.Key technologies for demand response in smart grid[J].Proceedings of the CSEE,2014,34(22):3576-3589(in Chinese).
[4]Yang H T,Chen S C,Peng P C.Genetic k-means-algorithm-based classification of direct load-control curves[J].IEE ProceedingsGeneration,Transmission and Distribution,2005,152(4):489-495.
[5]陈宏义,李存斌,施立刚.基于聚类分析的短期负荷智能预测方法研究[J].湖南大学学报:自然科学版,2014,41(5):94-98.Chen Hongyi,Li Cunbin,Shi Ligang.A new forecasting approach for short-term load intelligence based on cluster method[J].Journal of Hunan University:Natural Sciences,2014,41(5):94-98(in Chinese).
[6]Ferreira A M S,Cavalcante C A M T,Fontes C H O,et al.A new method for pattern recognition in load profiles to support decision-making in the management of the electric sector[J].International Journal of Electrical Power&Energy Systems,2013,53:824-831.
[7]Nizar A H,Dong Z Y,Wang Y.Power utility nontechnical loss analysis with extreme learning machine method[J].IEEE Transactions on Power Systems,2008,23(3):946-955.
[8]Chicco G,Napoli R,Piglione F,et al.Load pattern-based classification of electricity customers[J].IEEE Transactions on Power Systems,2004,19(2):1232-1239.
[9]曾博,张建华,丁蓝,等.改进自适应模糊C均值算法在负荷特性分类的应用[J].电力系统自动化,2011,35(12):42-46.Zheng Bo,Zhang Jianhua,Ding Lan,et al.An improved adaptive fuzzy c-means algorithm for load characteristics classification[J].Automation of Electric Power Systems,2011,35(12):42-46(in Chinese).
[10]黄毅成,杨洪耕.改进遗传K均值算法在负荷特性分类的应用[J].电力系统及其自动化学报,2014,26(7):70-75.Huang Yicheng,Yang Honggeng.Application of improved genetic and K-means algorithm on load characteristics classification[J].Proceedings of the CSU-EPSA,2014,26(7):70-75(in Chinese).
[11]Kim Y I,Ko J M,Choi S H.Methods for generating TLPs(typical load profiles)for smart grid-based energy programs[C]//2011 IEEE Symposium on Computational Intelligence Applications in Smart Grid(CIASG).Paris,France:IEEE,2011:1-6.
[12]张斌,庄池杰,胡军,等.结合降维技术的电力负荷曲线集成聚类算法[J].中国电机工程学报,2015,35(15):3741-3749.Zhang Bin,Zhuang Chijie,Hu Jun,et al.Ensemble clustering algorithm combined with dimension reduction techniques for power load profiles[J].Proceedings of the CSEE,2015,35(15):3741-3749(in Chinese).
[13]Yu Zuwei.A temperature match based optimization method for daily load prediction considering DLC effect[J].IEEE Transactions on Power Systems,1996,11(2):728-733.
[14]赵岩,李磊,刘俊勇,等.上海电网需求侧负荷模式的组合识别模型[J].电网技术,2010,34(1):145-151.Zhao Yan,Li Lei,Liu Junyong,et al.Combinational recognition model for demand side load profile in shanghai power grid[J].Power System Technology,2010,34(1):145-151(in Chinese).
[15]张智晟,孙雅明,张世英,等.基于数据挖掘多层次细节分解的负荷序列聚类分析[J].电网技术,2006,30(2):51-56.Zhang Zhisheng,Sun Yaming,Zhang Shiying,et al.Clustering analysis of electric load series using clustering algorithm of multi-hierarchy and detailed decomposition based on data mining[J].Power System Technology,2006,30(2):51-56(in Chinese).
[16]贾慧敏,何光宇,方朝雄,等.用于负荷预测的层次聚类和双向夹逼结合的多层次聚类法[J].电网技术,2007,31(23):33-36.Jia Huimin,He Guangyu,Fang Chaoxiong,et al.Load forecasting by multi-hierarchy clustering combining hierarchy clustering with approaching algorithm in two directions[J].Power System Technology,2007,31(23):33-36(in Chinese).
[17]黄宇腾,侯芳,周勤,等.一种面向需求侧管理的用户负荷形态组合分析方法[J].电力系统保护与控制,2013,41(13):20-25.Huang Yuteng,Hou Fang,Zhou Qin,et al.A new combinational electrical load analysis method for demand side management[J].Power System Protection and Control,2013,41(13):20-25(in Chinese).
[18]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(in Chinese).
[19]彭勃,张逸,熊军,等.结合负荷形态指标的电力负荷曲线两步聚类算法[J].电力建设,2016,37(6):96-102.Peng Bo,Zhang Yi,Xiong Jun,et al.A two-step clustering algorithm combined with load shape index for power load curve[J].Electric Power Construction,2016,37(6):96-102(in Chinese).
[20]周世兵,徐振源,唐旭清.新的K-均值算法最佳聚类数确定方法[J].计算机工程与应用,2010,46(16):27-31.Zhou Shibing,Xu Zhenyuan,Tang Xuqing.New method for determining optimal number of clusters in k-means clustering algorithm[J].Computer Engineering and Applications,2010,46(16):27-31(in Chinese).
[21]刘思,李林芝,吴浩,等.基于特性指标降维的日负荷曲线聚类分析[J].电网技术,2016,40(3):797-803.Liu Si,Li Linzhi,Wu Hao,et al.Cluster analysis of daily load curves using load pattern indexes to reduce dimensions[J].Power System Technology,2016,40(3):797-803(in Chinese).
[22]Wilson E.Commercial and residential hourly load profiles for all TMY3 locations in the United States[EB/OL].(2013-07-02)[2017-05-11].http://en.openei.org/datasets/dataset/commercialand-residentialhourly-load-profiles-for-all-tmy3-locations-in-theunited-states.