基于改进K-means算法的电网运行断面相似性匹配研究
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摘要
为简化电网工作票制定过程中复杂的方案校验工作,降低对电网调度人员工作经验的依赖,提出利用断面特征提取进行电网历史运行断面相似性匹配的方法。首先根据系统运行特点和数据存储格式,采用决策树模型提取、筛选特征变量;然后基于改进半监督K-means算法对历史运行断面进行初步相似性聚类,获取有效样本,降低数据规模;最后利用相似性匹配指标体系在聚类结果中为系统当前运行断面匹配到最有参考和利用价值的历史运行断面及其对应决策信息。仿真算例表明,所提方法可以很好地完成运行断面的相似性匹配工作。
In order to simplify the complex scheme checking work in the process of making power grid work tickets and reduce the dependence on work experience of power grid dispatchers,a running section similarity matching method of power grid by section characteristic extraction is proposed. Firstly,the decision tree model is adopted to exact and screen the characteristic variables according to the system operation characteristic and data storage format. Then,initial similarity clustering is carried out based on the improved semi-supervised K-means algorithm to obtain effective samples and reduce the data scale. Finally,a similarity matching index system is used to match the most valuable and useful historical running section and its corresponding decision information for the system current running section in the clustering results. Simulation cases show that the proposed method can well match the similarity of running sections.
In order to simplify the complex scheme checking work in the process of making power grid work tickets and reduce the dependence on work experience of power grid dispatchers,a running section similarity matching method of power grid by section characteristic extraction is proposed. Firstly,the decision tree model is adopted to exact and screen the characteristic variables according to the system operation characteristic and data storage format. Then,initial similarity clustering is carried out based on the improved semi-supervised K-means algorithm to obtain effective samples and reduce the data scale. Finally,a similarity matching index system is used to match the most valuable and useful historical running section and its corresponding decision information for the system current running section in the clustering results. Simulation cases show that the proposed method can well match the similarity of running sections.
引文
[1]中国电机工程学会电力信息化专业委员会.中国电力大数据发展白皮书[M].北京:中国电机工程学会,2013:16.
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[15]冯治鸿,周双喜.大规模电力系统电压失稳区的确定方法[J].中国电机工程学报,1997,17(3):152-156.FENG Zhihong,ZHOU Shuangxi.Determination of voltage collapse areas in large scale power system[J].Proceedings of the CSEE,1997,17(3):152-156.
[16]周双喜.电力系统电压稳定性及其控制[M].北京:中国电力出版社,2004.
[17]荣盘祥,王继尧,金鸿章.复杂系统的脆性与系统演化分析[J].电机与控制学报,2004,8(2):142-144.RONG Panxiang,WANG Jiyao,JIN Hongzhang.The analysis of system evolvement based on complex system[J].Electric Machines and Control,2004,8(2):142-144.
[18]曹一家,王光增,曹丽华,等.基于潮流熵的复杂电网自组织临界态判断模型[J].电力系统自动化,2011,35(7):1-6.CAO Yijia,WANG Guangzeng,CAO Lihua,et al.An identification model for self-organized criticality of power grids based on power flow entropy[J].Automation of Electric Power Systems,2011,35(7):1-6.
[19]张文朝,顾雪平.应用变异系数法和逼近理想解排序法的风电场综合评价[J].电网技术,2014,38(10):2741-2746.ZHANG Wenchao,GU Xueping.Comprehensive evaluation of wind farms using variation coefficient method and technique for order preference by similarity to ideal solution[J].Power System Technology,2013,38(10):2741-2746.
[2]黄天恩,孙宏斌,郭庆来,等.基于电网运行仿真大数据的知识管理和超前安全预警[J].电网技术,2015,39(11):3080-3087.HUANG Tianen,SUN Hongbin,GUO Qinglai,et al.Knowledge management and security early warning based on big simulation data in power grid operation[J].Power System Technology,2015,39(11):3080-3087.
[3]苗新,张冬霞,孙德栋.在配电网中应用大数据的机遇与挑战[J].电网技术,2015,39(11):3022-3127.MIAO Xin,ZHANG Dongxia,SUN Dedong.The opportunity and chanllenge of big data’s application in power distribution networks[J].Power System Technology,2015,39(11):3022-3127.
[4]黄彦浩,于之虹,史东宇,等.基于海量在线历史数据的大电网快速判稳策略[J].中国电机工程学报,2016,36(3):596-603.HUANG Yanhao,YU Zhihong,SHI Dongyu,et al.Strategy of huge electric power system stability quick judgment based on massive historical online data[J].Proceedings of the CSEE,2016,36(3):596-603.
[5]石连山.面向时间过程的特征模式提取及N-1静态安全分析研究[D].哈尔滨:哈尔滨工业大学,2006.SHI Lianshan.Time process-oriented feature-model distilling and N-1 static security analysis research[D].Harbin:Harbin Institute of Technology,2006.
[6]陈中,梁欢利.面向时间过程的概率性静态安全分析法[J].电气技术,2016,35(20):44-49.CHEN Zhong,LIANG Huanli.Time process-oriented probabilistic static security analysis method[J].Electric Technology,2016,35(20):44-49.
[7]张宝群.面向时间过程的负荷特征断面提取与日前交易的研究[D].哈尔滨:哈尔滨工业大学,2006.ZHANG Baoqun.Time process-oriented research of distilling characteristic load cross section and day-ahead trade[D].Harbin:Harbin Institute of Technology,2006.
[8]余加喜.面向时间过程的电网静态安全分析与日发电计划研究[D].哈尔滨:哈尔滨工业大学,2009.YU Jiaxi.Research on time process-oriented power system static security analysis and daily generation scheduling[D].Harbin:Harbin Institute of Technology,2009.
[9]和怡.基于数据挖掘的静态电压稳定在线评估[D].北京:北京交通大学,2017.HE Yi.Online static voltage stability evaluation based on data mining[D].Beijing:Beijing Jiaotong University,2017.
[10]肖琪.基于优化K-means算法的电力负荷分类研究[D].大连:大连理工大学,2015.XIAO Qi.Research on power load classification based on optimized K-means algorithm[D].Dalian:Dalian University of Technology,2015.
[11]DRIAS H,CHERIF N F,KECHID A.A hybrid clustering algorithm based on k-means and k-medoids[M].Berlin,Germany:Springer International Publishing,2016:22-24.
[12]韩俊,谈健,黄河,等.基于改进K-means算法的供电块划分方法[J].电力自动化设备,2015,35(6):123-128.HAN Jun,TAN Jian,HUANG He,et al.Power-supplying block partition based on improved K-means clustering algorithm[J].Electric Power Automation Equipment,2015,35(6):123-128.
[13]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.
[14]冯治鸿,刘取.多机电力系统电压静态稳定性的特征结构分析法[J].清华大学学报:自然科学版,1991,31(4):19-27.FENG Zhihong,LIU Qu.An eigen structure analysis method of the steady-state voltage stability in multimachine system[J].Journal of Tsinghua University,1991,31(4):19-27.
[15]冯治鸿,周双喜.大规模电力系统电压失稳区的确定方法[J].中国电机工程学报,1997,17(3):152-156.FENG Zhihong,ZHOU Shuangxi.Determination of voltage collapse areas in large scale power system[J].Proceedings of the CSEE,1997,17(3):152-156.
[16]周双喜.电力系统电压稳定性及其控制[M].北京:中国电力出版社,2004.
[17]荣盘祥,王继尧,金鸿章.复杂系统的脆性与系统演化分析[J].电机与控制学报,2004,8(2):142-144.RONG Panxiang,WANG Jiyao,JIN Hongzhang.The analysis of system evolvement based on complex system[J].Electric Machines and Control,2004,8(2):142-144.
[18]曹一家,王光增,曹丽华,等.基于潮流熵的复杂电网自组织临界态判断模型[J].电力系统自动化,2011,35(7):1-6.CAO Yijia,WANG Guangzeng,CAO Lihua,et al.An identification model for self-organized criticality of power grids based on power flow entropy[J].Automation of Electric Power Systems,2011,35(7):1-6.
[19]张文朝,顾雪平.应用变异系数法和逼近理想解排序法的风电场综合评价[J].电网技术,2014,38(10):2741-2746.ZHANG Wenchao,GU Xueping.Comprehensive evaluation of wind farms using variation coefficient method and technique for order preference by similarity to ideal solution[J].Power System Technology,2013,38(10):2741-2746.