基于自适应k-means++算法的电力负荷特性分析
|
摘要
运用数据挖掘中的聚类算法对电力负荷曲线进行聚类分析,提炼电力负荷曲线之间的共性特征与差异特征,在负荷模型实用化方面有重要应用价值,可以帮助分析用户用电规律,指导电网规划及实时调度。本文提出了一种自适应k-means++负荷特性聚类算法,综合不同聚类数时的聚类结果验证了数据集里各样本的相似性,通过迭代图切分的方法确定了最佳聚类数,避免了人为设定电力用户日负荷曲线聚类数不恰当导致的单一聚类结果的过大偏差,提高了负荷分类的精确性。算例结果验证了该算法的可行性和有效性,表明该算法求最佳聚类数的准确性高、鲁棒性好。
The clustering technique in data mining has been widely applied for load curves clustering. Load curves clustering helps refining common and different characteristics among loads,which has important application values for the practicality of load model. On the other hand,it helps analyzing load patterns,guiding planning and real-time dispatching of power systems. In this paper,an adaptive k-means + + algorithm is proposed,which synthesizes results of different cluster numbers to verify the similarity of the samples in the dataset,and adopts an iterative graph-partitioning method to identify the optimal cluster number. The improved algorithm avoids excessive deviation of single clustering result caused by inappropriate cluster number of daily load curves,which could improve the accuracy of load curves classification. Numerical experiments verify the feasibility and effectiveness of the proposed algorithm,and show that the accuracy of algorithm is high and robustness is good when solving the best cluster number.
The clustering technique in data mining has been widely applied for load curves clustering. Load curves clustering helps refining common and different characteristics among loads,which has important application values for the practicality of load model. On the other hand,it helps analyzing load patterns,guiding planning and real-time dispatching of power systems. In this paper,an adaptive k-means + + algorithm is proposed,which synthesizes results of different cluster numbers to verify the similarity of the samples in the dataset,and adopts an iterative graph-partitioning method to identify the optimal cluster number. The improved algorithm avoids excessive deviation of single clustering result caused by inappropriate cluster number of daily load curves,which could improve the accuracy of load curves classification. Numerical experiments verify the feasibility and effectiveness of the proposed algorithm,and show that the accuracy of algorithm is high and robustness is good when solving the best cluster number.
引文
[1]金小明,门锟,李勇,等.单一综合负荷模型等效负荷群的适用性[J].南方电网技术,2015,9(4):96-102.JIN Xiaoming,MEN Kun,LI Yong,et al. Feasibility of a single composite load model representing composite loads in an area[J].Southern Power System Technology,2015,9(4):96-102.
[2]李颖,贺仁睦,徐衍会.广东电网基于PMU的负荷模型参数辨识研究[J].南方电网技术,2009,3(1):16-19.LI Ying,HE Renmu,XU Yanhui. Research of PMU-based load model parameters identification of Guangdong Power Grid[J].Southern Power System Technology,2009,3(1):16-19.
[3]罗敏,周尚礼,艾芊,等.广东电网基于BPA的实用负荷模型的建立与校验[J].南方电网技术,2010,4(s1):162-166.LUO Min,ZHOU Shangli,AI Qian,et al. Construction and verification of the BPA-based practical load model of Guangdong Power Grid[J]. Southern Power System Technology,2010,4(s1):162-166.
[4]李培强,李欣然,陈辉华,等.基于模糊聚类的电力负荷特性的分类与综合[J].中国电机工程学报,2005,25(24):73-78.LI Peiqiang,LI Xinran,CHEN Huihua,et al. The characteristics classification and synthesis of power load based on fuzzy clustering[J]. Proceeding of the CSEE,2005,25(24):73-78.
[5]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.ZENG 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.
[6]周开乐,杨善林.基于改进模糊C均值算法的电力负荷特性分类[J].电力系统保护与控制,2012,40(22):58-63.ZHOU Kaile, YANG Shanlin. An improved fuzzy C-means algorithm for power load characteristics classification[J]. Power System Protection and Control,2012,40(22):58-63.
[7]陈明辉,王珂,蔡莹,等.基于数据驱动的母线负荷特性分析[J].南方电网技术,2016,10(2):70-76.CHEN Minghui,WANG Ke,CAI Ying,et al. Bus load characteristics analysis based on data-driven method[J]. Southern Power System Technology,2016,10(2):70-76.
[8]罗滇生,杜乾,别少勇,等.基于负荷分解的居民差异化用电行为特性分析[J].电力系统保护与控制,2016,44(21):29-33.LUO Diansheng,DU Qian,BIE Shaoyong,et al. Analysis of differentiation residential electricity consumption characteristic based on power load decomposition[J]. Power System Protection and Control,2016,44(21):29-33.
[9]李欣然,姜学皎,钱军,等.基于用户日负荷曲线的用电行业分类与综合方法[J].电力系统自动化,2010,34(10):56-61.LI Xinran,JIANG Xuejiao,QIAN Jun,et al. A classifying and synthesizing method of power consumer industry based on the daily load profile[J]. Automation of Electric Power System,2010,34(10):56-61.
[10] KWAC J,FLORA J,RAJAGOPAL R. Household energy consumption segmentation using hourly data[J]. IEEE Transactions on Smart Grid,2014,5(1):420-430.
[11] MUTANEN A,RUSKA M,REPO S,et al. Customer classification and load profiling method for distribution systems[J]. IEEE Transactions on Power Delivery,2011,26(3):1755-1763.
[12]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.
[13] TAN P N,STEINBACH M,KUMAR V.数据挖掘导论[M].范明,范宏建,译.北京:人民邮电出版社,2011.
[14]赵莉,候兴哲,胡君,等.基于改进k-means算法的海量智能用电数据分析[J].电网技术,2014,38(10):2715-2720.ZHAO Li,HOU Xingzhe,HU Jun,et al. Improved k-means algorithm based analysis on massive data of intelligent power utilization[J]. Power System Technology,2014,38(10):2715-2720.
[15] MOK P Y,HUANG H Q,KWOK Y L,et al. A robust adaptive clustering analysis method for automatic identification of clusters[J]. Pattern Recognition,2012,45(8):3017-3033.
[16]程祥.基于负荷量测数据的电力负荷聚类方法研究[D].杭州:浙江大学,2017.
[17]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]. Proceeding of the CSEE,2015,35(15):3741-3749.
[18]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 combination electrical load analysis method for demand side management[J]. Power System Protection and Control,2013,41(13):20-25.
[2]李颖,贺仁睦,徐衍会.广东电网基于PMU的负荷模型参数辨识研究[J].南方电网技术,2009,3(1):16-19.LI Ying,HE Renmu,XU Yanhui. Research of PMU-based load model parameters identification of Guangdong Power Grid[J].Southern Power System Technology,2009,3(1):16-19.
[3]罗敏,周尚礼,艾芊,等.广东电网基于BPA的实用负荷模型的建立与校验[J].南方电网技术,2010,4(s1):162-166.LUO Min,ZHOU Shangli,AI Qian,et al. Construction and verification of the BPA-based practical load model of Guangdong Power Grid[J]. Southern Power System Technology,2010,4(s1):162-166.
[4]李培强,李欣然,陈辉华,等.基于模糊聚类的电力负荷特性的分类与综合[J].中国电机工程学报,2005,25(24):73-78.LI Peiqiang,LI Xinran,CHEN Huihua,et al. The characteristics classification and synthesis of power load based on fuzzy clustering[J]. Proceeding of the CSEE,2005,25(24):73-78.
[5]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.ZENG 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.
[6]周开乐,杨善林.基于改进模糊C均值算法的电力负荷特性分类[J].电力系统保护与控制,2012,40(22):58-63.ZHOU Kaile, YANG Shanlin. An improved fuzzy C-means algorithm for power load characteristics classification[J]. Power System Protection and Control,2012,40(22):58-63.
[7]陈明辉,王珂,蔡莹,等.基于数据驱动的母线负荷特性分析[J].南方电网技术,2016,10(2):70-76.CHEN Minghui,WANG Ke,CAI Ying,et al. Bus load characteristics analysis based on data-driven method[J]. Southern Power System Technology,2016,10(2):70-76.
[8]罗滇生,杜乾,别少勇,等.基于负荷分解的居民差异化用电行为特性分析[J].电力系统保护与控制,2016,44(21):29-33.LUO Diansheng,DU Qian,BIE Shaoyong,et al. Analysis of differentiation residential electricity consumption characteristic based on power load decomposition[J]. Power System Protection and Control,2016,44(21):29-33.
[9]李欣然,姜学皎,钱军,等.基于用户日负荷曲线的用电行业分类与综合方法[J].电力系统自动化,2010,34(10):56-61.LI Xinran,JIANG Xuejiao,QIAN Jun,et al. A classifying and synthesizing method of power consumer industry based on the daily load profile[J]. Automation of Electric Power System,2010,34(10):56-61.
[10] KWAC J,FLORA J,RAJAGOPAL R. Household energy consumption segmentation using hourly data[J]. IEEE Transactions on Smart Grid,2014,5(1):420-430.
[11] MUTANEN A,RUSKA M,REPO S,et al. Customer classification and load profiling method for distribution systems[J]. IEEE Transactions on Power Delivery,2011,26(3):1755-1763.
[12]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.
[13] TAN P N,STEINBACH M,KUMAR V.数据挖掘导论[M].范明,范宏建,译.北京:人民邮电出版社,2011.
[14]赵莉,候兴哲,胡君,等.基于改进k-means算法的海量智能用电数据分析[J].电网技术,2014,38(10):2715-2720.ZHAO Li,HOU Xingzhe,HU Jun,et al. Improved k-means algorithm based analysis on massive data of intelligent power utilization[J]. Power System Technology,2014,38(10):2715-2720.
[15] MOK P Y,HUANG H Q,KWOK Y L,et al. A robust adaptive clustering analysis method for automatic identification of clusters[J]. Pattern Recognition,2012,45(8):3017-3033.
[16]程祥.基于负荷量测数据的电力负荷聚类方法研究[D].杭州:浙江大学,2017.
[17]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]. Proceeding of the CSEE,2015,35(15):3741-3749.
[18]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 combination electrical load analysis method for demand side management[J]. Power System Protection and Control,2013,41(13):20-25.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |