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聚类分析及其在电力系统中的应用综述
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  • 英文篇名:Survey of Cluster Analysis and Its Application in Power System
  • 作者:李君卫 ; 汤亚芳 ; 郝正航 ; 冒国龙 ; 姜有泉
  • 英文作者:LI Junwei;TANG Yafang;HAO Zhenghang;MAO Guolong;JIANG Youquan;College of Electric Engineering,Guizhou University;
  • 关键词:聚类分析 ; 数据挖掘 ; AMI ; 负荷预测 ; 电能质量 ; 局部放电 ; 需求响应
  • 英文关键词:clustering analysis;;data mining;;AMI;;load forecasting;;power quality;;partial discharge;;demand response
  • 中文刊名:XDDL
  • 英文刊名:Modern Electric Power
  • 机构:贵州大学电气工程学院;
  • 出版日期:2018-12-10 15:15
  • 出版单位:现代电力
  • 年:2019
  • 期:v.36;No.160
  • 基金:国家自然科学基金项目(51467003)
  • 语种:中文;
  • 页:XDDL201903001
  • 页数:10
  • CN:03
  • ISSN:11-3818/TM
  • 分类号:5-14
摘要
随着高级测量体系(AMI)在智能电网中的大量使用,电网产生海量的样本信息数据,使用聚类分析方法可以获得详尽的电力系统运行信息。对电力系统中常用的经典型聚类方法和混合型聚类方法进行了概括,并总结了聚类结果的评价指标;对聚类分析在电力系统的负荷预测、电能质量扰动分析、孤岛检测、局部放电和需求响应等领域的应用现状进行了分析;展望了聚类分析技术在电力系统中的研究与发展前景。
        With the extensive use of advanced metering infrastructure(AMI)in the smart grid,the power grid produces massive sample information data,and the detailed information of power system operation can be obtained by clustering analysis.In this paper,the classical clustering and hybrid clustering methods commonly used in power system are generalized,and the evaluation index of clustering results is summarized.Besides,the application of cluster analysis in power system load forecasting,power quality disturbance analysis,islanding detection,partial discharge and demand response are analyzed.Finally,the research and development prospect of cluster analysis technology in power system is forecasted.
引文
[1]CHIO H J,PARK S,KO W,et al.Implementation of AMI in city energy management systems[C]//2017 14th International Multi-conference on Systems,Signals&Devices(SSD),Marrakech,Morocco,2017:372-376.
    [2]王德生.世界主要市场智能电表发展前景[EB/OL].(2017-5-24).http://www.istis.sh.cn/list/list.aspx?id=10623.
    [3]电缆网.2016-20年中国智能电表安装量将增5.75%[EB/OL].(2016-05-18).http://news.cableabc.com/gc/20160518340614.html.
    [4]LUO F J,DONG Z Y,CHEN G,et al.Advanced pattern discovery-based fuzzy classification method for power system dynamic security assessment[J].IEEE Transactions on Industrial Informatics,2015,11(2):416-426.
    [5]张素香,赵丙镇,王风雨,等.海量数据下的电力负荷短期预测[J].中国电机工程学报,2015,35(1):37-42.ZHANG Suxiang,ZHAO Bingzhen,WANGFengyu,et al.Short-term power load forecasting based on big data[J].Proceedings of the CSEE,2015,35(1):37-42.
    [6]GRANELL R,AXON C J,WALLOM D C H.Impacts of raw data temporal resolution using selected clustering methods on residential electricity load profiles[J].IEEE Transactions on Power Systems,2015,30(6):3217-3224.
    [7]李夏林,刘雅娟,朱武.基于配电网的复合电压暂降源分类与识别新方法[J].电力系统保护与控制,2017,45(2):131-139.LI Xialin,LIU Yajuan,ZHU Wu.A new method to classify and identify composite voltage sag sources in distribution network[J].Power System Protection and Control.2017,45(2):131-139.
    [8]邱海峰,陈兵,袁晓冬,等.基于动态时间弯曲距离的电压暂降源辨识方法[J].电力系统保护与控制,2017,45(13):7-13.QIU Haifeng,CHEN Bing,YUAN Xiaodong,et al.Identification of voltage sag sources based on DTW[J].Power System Protection and Control,2017,45(13):7-13.
    [9]陈磊磊.不同距离测度的k-means文本聚类研究[J].软件,2015,36(1):56-61.CHEN Leilei.Text clustering study with k-means algorithm of different distance measures[J].Computer Engineering&Software.2015,36(1):56-61.
    [10]彭显刚,赖家文,陈奕.基于聚类分析的客户用电模式智能识别方法[J].电力系统保护与控制,2014,42(19):68-73.PENG Xiangang,LAI Jiawen,CHEN Yi.Application of clustering analysis in typical power consumption profile analysis[J].Power System Protection and Control,2014,42(19):68-73.
    [11]孟建良,刘德超.一种基于Spark和聚类分析的辨识电力系统不良数据新方法[J].电力系统保护与控制,2016,44(3):85-91.MENG Jianliang,LIU Dechao.A new method for identifying bad data of power system based on Spark and clustering analysis[J].Power System Protection and Control.2016,44(3):85-91.
    [12]高小力,张智博,田启明,等.基于HS-Clustering的风电场机组分组功率预测[J].现代电力,2017,34(3):12-18.GAO Xiaoli,ZHANG Zhibo,TIAN Qiming,et al.Wind power forecasting for clustering wind turbines based on HS-clustering[J].Modern Electric Power.2017,34(3):12-18.
    [13]李昀昊,王建学,王秀丽.基于混合聚类分析的电力系统网损评估方法[J].电力系统自动化,2016,40(1):60-65.LI Yunhao,WANG Jianxue,WANG Xiuli.A power system network loss evaluation method based on hybrid clustering analysis[J].Automation of Electric Power Systems.2016,40(1):60-65.
    [14]田力,向敏.基于密度聚类技术的电力系统用电量异常分析算法[J].电力系统自动化,2017,41(5):64-70.TIAN Li,XIANG Min.Abnormal power consumption analysis based on density-based spatial clustering of applications with noise in power systems[J].Automation of Electric Power Systems.2017,41(5):64-70.
    [15]YANG J J,ZHAO J H,WEN F S,et al.A model of customizing electricity retail prices based on load profile clustering analysis[J].IEEE Transactions on Smart Grid.2018,doi:10.1109/TSG.2018.2825335.
    [16]WU Z R,DONG X Z,LIU Z W.et al.Power system bad load data detection based on an improved fuzzy c-means clustering algorithm[C]//2017IEEEPower&Energy Society General Meeting,Chicago,America,2017:1-5.
    [17]WANG K,LI J Z,ZHANG S Q,et al.A hybrid algorithm based on S transform and affinity propagation clustering for separation of two simultaneously artificial partial discharge sources[J].IEEE Transactions on Dielectrics and Electrical Insulation,2015,22(2):1042-1060.
    [18]成乐祥,季丽.基于加权k-means聚类和遗传算法的变电站规划[J].江苏电机工程,2016,35(6):9-12.CHENG Lexiang,JI Li.Substation planning based on weighted k-means cluster algorithm and genetic algorithm[J].Jiangsu Electrical Engineering.2016,35(6):9-12.
    [19]刘艳,陈丽安.基于SOM的真空断路器机械故障诊断[J].电工技术学报,2017,32(5):49-54.LIU Yan,CHEN Lian.Mechanical fault diagnosis of vacuum circuit breaker based on SOM[J].Transactions of China Electrotechnical Society,2017,32(5):49-54.
    [20]6)INKAYA T,KAYALGIL S,ZDEMIREL N E.Ant colony optimization based clustering methodology[J].Applied Soft Computing,2015,28:301-311.
    [21]VARGA E D,BERETKA S F,NOCE C,et al.Robust real-time load profile encoding and classification framework for efficient power systems operation[J].IEEE Transactions on Power Systems,2015,30(4):1897-1904.
    [22]FIRUZI K,VAKILIAN M,DARABAD V P,et al.A novel method for differentiating and clustering multiple partial discharge sources using S transform and bag of words feature[J].IEEE Transactions on Dielectrics and Electrical Insulation,2017,24(6):3694-3702.
    [23]LIU X F,SHANG L Q.Power system load forecasting by improved principal component analysis and neural network[C]//2016IEEE International Conference on High Voltage Engineering and Application(ICHVE),Chengdu,China,2016:1-4.
    [24]AL-OTAIBI R,JIN N L,WILCOX T,et al.Feature construction and calibration for clustering daily load curves from smart-meter data[J].IEEETransactions on Industrial Informatics,2016,12(2):645-654.
    [25]YANG Q,HU C Z,ZHENG N G.Data-driven diagnosis of nonlinearly mixed mechanical faults in wind turbine gear box[J].IEEE Internet of Things Journal,2018,5(1):466-467.
    [26]DING R,WANG Q,DANG Y,et al.Yading:fast clustering of large-scale time series data[J].Proceedings of the VLDB Endowment,2015,8(5):473-484.
    [27]沈沉,秦建,盛万兴,等.基于小波聚类的配变短期负荷预测方法研究[J].电网技术,2016,40(2):521-526.SHEN Chen,QIN Jian,SHENG Wanxing,et al.Research on short-term load forecasting method of distribution transformer based on wavelet clustering[J].Power System Technology.2016,40(2):521-526.
    [28]祖向荣,田敏,白焰.基于模糊聚类与函数小波核回归的短期负荷预测方法[J].电力自动化设备,2016,36(10):134-140,165.ZU Xiangrong,TIAN Min,BAI Yan.Short-term load forecasting based on fuzzy clustering and functional wavelet-kernel regression[J].Electric Power Automation Equipment.2016,36(10):134-140,165.
    [29]PANAPAKIDIS I,ALEXIADIS M,PAPAGIAN-NIS G.Evaluation of the performance of clustering algorithms for a high voltage industrial consumer[J].Engineering Applications of Artificial Intelligence,2015,38:1-13.
    [30]MAHMOUDI-KOHAN N,MOGHADDAM M P,BIDAKI S M.Evaluating performance of WFA kmeans and modified follow the leader methods for clustering load curves[C]//Power Systems Conference and Exposition,2009.PSCE09.IEEE/PES.IEEE,Seattle,WA,2009:1-5.
    [31]林顺富,谢潮,汤波,等.数据挖掘在电能质量监测数据分析中的应用[J].电测与仪表,2017,54(9):46-51.LIN Shunfu,XIE Chao,TANG Bo,et al.The data mining application in the power quality monitoring data analysis[J].Electrical Measurement&Instrumentation,2017,54(9):46-51.
    [32]ZHANG T F,ZHANG G Q,LU J,et al.A new index and classification approach for load pattern analysis of large electricity customers[J].IEEE Transactions on Power Systems,2012,27(1):153-160.
    [33]PANAPAKIDIS I P,ALEXIADIS M C,PAPAGI-ANNIS G K.Deriving the optimal number of clusters in the electricity consumer segmentation procedure[C]//2013 10th International Conference on the European Energy Market(EEM),Stockholm,Sweden,2013:1-8.
    [34]CHICCO G,NAPOLI R,PIGLIONE F.Comparisons among clustering techniques for electricity customer classification[J].IEEE Transactions on Power Systems,2006,21(2):933-940.
    [35]KOHAN N M,MOGHADDAM M P,BIDAKI S M,et al.Comparison of modified k-means and hierarchical algorithms in customers load curves clustering for designing suitable tariffs in electricity market[C]//200843rd International Universities Power Engineering Conference,Padova,Italy,2008:1-5.
    [36]HUANG X H,YE Y M,XIONG L Y,et al.Time series k-means:A new k-means type smooth subspace clustering for time series data[J].Information Sciences,2016,367:1-13.
    [37]曾楠,许元斌,罗义旺,等.基于分布式聚类模型的电力负荷特性分析[J].现代电力,2018,35(1):71-77.ZENG Nan,XU Yuanbin,LUO Yiwang,et al.A-nalysis of power load characteristics based on distributed clustering model[J].Modern Electric Power.2018,35(1):71-77.
    [38]QUILUMBA F L,LEE W J,HUANG H,et al.Using smart meter data to improve the accuracy of intraday load forecasting considering customer behavior similarities[J].IEEE Transactions on Smart Grid,2015,6(2):911-918.
    [39]WANG Y,YANG J F.Kernel-based clustering for short-term load forecasting[C]//10th International Conference on Advances in Power System Control,Operation&Management(APSCOM 2015),Hong Kong,China,2015:1-6.
    [40]LU Y,ZHANG T K,ZENG Z M,et al.An improved RBF neural network for short-term load forecast in smart grids[C]//2016IEEE International Conference on Communication Systems(ICCS),Shenzhen,China,2016:1-6.
    [41]程启明,张强,程尹曼,等.基于密度峰值层次聚类的短期光伏功率预测模型[J].高电压技术,2017,43(4):1214-1222.CHENG Qiming,ZHANG Qiang,CHENG Yinman,et al.Short-term photovoltaic power prediction model based on hierarchical clustering of density peaks algorithm[J].High Voltage Engineering.2017,43(4):1214-1222.
    [42]于秋玲,许长清,李珊,等.基于模糊聚类和支持向量机的短期光伏功率预测[J].电力系统及其自动化学报,2016,28(12):115-118,12.YU Qiuling,XU Changqing,LI Shan,et al.Application of fuzzy clustering algorithm and support vector machine to short-term forecasting of PV power[J].Proceedings of the CSU-EPSA.2016,28(12):115-118,12.
    [43]徐志超,杨玲君,李晓明.基于聚类改进S变换与直接支持向量机的电能质量扰动识别[J].电力自动化设备,2015,35(7):50-58,73.XU Zhichao,YANG Lingjun,LI Xiaoming.Power quality disturbance identification based on clusteringmodified S-transform and direct support vector machine[J].Electric Power Automation Equipment.2015,35(7):50-58,73.
    [44]韩玉环,赵庆生,郭贺宏,等.基于FCM的暂态电能质量扰动识别[J].电力系统保护与控制,2016,44(9):62-68.HAN Yuhuan,ZHAO Qingsheng,GUO Hehong,et al.Identification of transient power quality disturbances based on FCM[J].Power System Protection and Control,2016,44(9):62-68.
    [45]SEERA M,LIM C P,LOO C K,et al.Power quality analysis using a hybrid model of the fuzzy min-max neural network and clustering tree[J],IEEE Transactions on Neural Networks and Learning Systems,2016,27(12):2760-2767.
    [46]鲍永胜,郝峰杰,徐建忠,等.GIS局部放电脉冲分类特征提取算法[J].电工技术学报,2016,31(9):181-188.BAO Yongsheng,HAO Fengjie,XU Jianzhong,et al.Classification feature extraction algorithm for GISpartial discharge pulses[J].Transactions of China Electrotechnical Society.2016,31(9):181-188.
    [47]陈攀,姚陈果,廖瑞金,等.分频段能量谱及马氏聚类算法在开关柜局部放电模式识别中的应用[J].高电压技术,2015,41(10):3332-3341.CHEN Pan,YAO Chenguo,LIAO Ruijin,et al.Application of signals separated band energy spectrum and Mahalanobis clustering algorithm for switchgear partial discharge pattern recognition[J].High Voltage Engineering,2015,41(10):3332-3341.
    [48]MAHDI M,GENC I.Defensive islanding using self organizing maps neural networks and hierarchical clustering[C]//2015IEEE Eindhoven PowerTech,Eindhoven,Netherlands,2015:1-5.
    [49]GHADIMI N.An adaptive neuro-fuzzy inference system for islanding detection in wind turbine as distributed generation[J].Complexity,2015,21(1):10-20.
    [50]雷敏,魏务卿,曾进辉,等.考虑需求响应的负荷控制对供电可靠性影响分析[J].电力系统自动化,2018,42(10):59-65.LEI Min,WEI Wuqing,ZENG Jinhui,et al.Influence analysis of load control considering demand response on power supply reliability[J].Automation of Electric Power Systems,2018,42(10):59-65.
    [51]LIN S F,LI F X,TIAN E W,et al.Clustering load profiles for demand response applications[J],IEEE Transactions on Smart Grid,2017,10(2):1599-1607.
    [52]陆俊,朱炎平,彭文昊,等.计及用电行为聚类的智能小区互动化需求响应方法[J].电力系统自动化,2017,41(17):113-120.LU Jun,ZHU Yanping,PENG Wenhao,et al.An interactive demand response method for intelligent community based on electrical behavior clustering[J].Automation of Electric Power Systems.2017,41(17):113-120.

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