基于电力大数据挖掘技术的变压器运行参数状态评估
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摘要
电力网络的设备参数的准确可靠,对于电力系统的分析计算有着重要作用,然而电力设备在实际运行时的参数并非出厂固定的常数,而是会根据环境和实际运行情况的变化而发生变化,而且很难被直接测量并用正确值替换。变压器作为电力传输中的枢纽设备,其运行参数的准确性对电力安全稳定分析更加重要。为准确反映变压器实时运行时真实参数值,文章在建立基于Γ型等值的变压器参数等值模型的基础上,提出了基于电力大数据密度聚类分析技术的变压器运行参数的估计方法,并利用经专家模块不良数据辨识后的SCADA系统量测数据进行变压器设备参数进行聚类和拟合,最终得出在一定允许误差范围内的设备运行实际参数。最后以徐州电网某变压器运行为例,经对SCADA系统数据进行聚类和拟合,数据结果验证了该方法的有效性,具有较好的工程实用性。
The accuracy and reliability of the equipment parameters of power network is very important for the analysis and calculation of power system. However,the parameters of power equipment in actual operation are not fixed constants,and they will change according to the changes of environment and actual operation conditions. And it's hard to measure directly and replace it with the correct value. As the hub equipment of power transmission,the accuracy of operation parameters of transformer is more important to the analysis of power safety and stability. In order to accurately reflect the real parameters of transformer in real operation,this paper presents a method for estimating transformer operating parameters based on power big data density clustering analysis technology,on the basis of establishing the transformer parameter equivalent model based on Γ type equivalence. The parameters of transformer equipment are estimated by using the measured data of SCADA system after bad data identification. Finally,the actual operating parameters of the equipment within a certain allowable error range are obtained. Taking a transformer in Xuzhou Power Grid as an example,by clustering and fitting the data of SCADA system,the results show that the method is effective and practical in engineering.
The accuracy and reliability of the equipment parameters of power network is very important for the analysis and calculation of power system. However,the parameters of power equipment in actual operation are not fixed constants,and they will change according to the changes of environment and actual operation conditions. And it's hard to measure directly and replace it with the correct value. As the hub equipment of power transmission,the accuracy of operation parameters of transformer is more important to the analysis of power safety and stability. In order to accurately reflect the real parameters of transformer in real operation,this paper presents a method for estimating transformer operating parameters based on power big data density clustering analysis technology,on the basis of establishing the transformer parameter equivalent model based on Γ type equivalence. The parameters of transformer equipment are estimated by using the measured data of SCADA system after bad data identification. Finally,the actual operating parameters of the equipment within a certain allowable error range are obtained. Taking a transformer in Xuzhou Power Grid as an example,by clustering and fitting the data of SCADA system,the results show that the method is effective and practical in engineering.
引文
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[14]纪怀猛.电力系统中的数据挖掘技术[J].长春工业大学学报(自然科学版),2014,(02):143-147.JI Huaimeng.Data mining technology in power system[J].Journal of Changchun University of Technology,2014,(02):143-147.
[15]王喜,赵宵凯,熊斌宇.一种基于数据挖掘技术的智能变电站故障诊断方法[J].陕西电力,2018,(04):39-43.WANG Xi,ZHAO Xiaokai,XIONG Binyu.Intelligent substation fault diagnosis method based on data mining technology[J].Shaanxi Electric Power,2018,(04):39-43.
[2]杨滢,孙宏斌,张伯明,等.集成于EMS中的参数估计软件的开发与应用[J].电网技术,2006,30(04):43-49YANG Ying,SUN Hongbin,ZHANG Boming,et al.Development and application of parameter estimation software integrated intoEMS[J].PowerSystemTechnology,2006,30(04):43-49.
[3]王茂海,齐霞,牛四清,等.基于相量测量单元实测数据的变压器参数在线估计方法[J].电力系统自动化,2011,35(13):61-65.WANG Maohai,QI Xia,NIU Siqing,et al.Online estimation of transformer parameters based on PMU measurements[J].Automation of Electric Power Systems,2011,35(13):61-65.
[4]何桦,柴京慧,卫志农,等.基于量测残差的改进参数估计方法[J].电力系统自动化,2007,31(04):33-36.HE Hua,CHAI Jinghui,WEI Zhinong,et al.Improved method of parameter estimation based on measurement residuals[J].Automation of Electric Power Systems,2007,31(04):33-36.
[5]W.LIUAND,S.LIM.Estimation of parameter errors from mea-surement residualsin state estimation[J].IEEE Transactions on Power Systems,1992,10(01):81-89.
[6]V.QUINTAMAAND,T.VANCUTSEM.Real-time processing of transformer tap positions[J].Canadian Electrical Engineering Journal,1987,12(04):171-180.
[7]A.REIGANDC.SLVAREZ.Off-line parameter estimation techniques for network model date timing in proceedings TAST-ED power high tech[C]//Valencia,Spain,1989:205-210.
[8]P.ZARCOANDA.GOMEZ.Off-line Determination of network parameters in state estimationin proceedings 12thpower system computation conference dresden[C]//Germany,Aug.1996:1207-1213.
[9]吴军英,刘明硕,常永娟,等.基于大数据的变压器设备状态及风险分析研究与应用[J].贵州电力技术,2017,20(10):22-30.WU Junying,LIU Mingshuo,CHANG Yongjuan,et al.Application and research of transformer equipment status and risk analysis based on big data[J].Guizhou Electric Power Technology,2017,20(10):22-30.
[10]李吉德,彭生刚,王承民.基于数据挖掘技术的电力网络参数估计方法研究[J].山东电力技术,2012,(01):18-24.LI Gide,PENG Shenggang,WANG Chengmin.Research on parameter estimation method of power network based on data Mining Technology[J].Shandong Electric Power Technology,2012,(01):18-24.
[11]李廷顺,谭文,刘泽三.基于大数据智能电网关键技术研究[J].电源技术,2017,41(8):1195-1197.LI Tingshun,TAN Wen,LIU Zesan.Research on key technologies of smart grid based on big data[J].Chinese Journal of Power Sources,2017,41(8):1195-1197.
[12]田拯,李新原,李连峰.数据挖掘技术在电力系统中的应用[J].河南科技,2017,(23):131-133.TIAN Zheng,LI Xinyuan,LI Lianfeng.The application of data mining technology in power system[J].Journal of Henan Science and Technology,2017,(23):131-133.
[13]陈星莺,张晓花,瞿峰,等.数据挖掘在电力系统中的应用综述[J].电力科学与技术学报,2007,22(3):51-56.CHEN XingyingZHANG Xiaohua,QU Feng et al.Survey on applications of data mining in power system[J].Jouranal of Electric Power Science and Technology,2007,22(3):51-56.
[14]纪怀猛.电力系统中的数据挖掘技术[J].长春工业大学学报(自然科学版),2014,(02):143-147.JI Huaimeng.Data mining technology in power system[J].Journal of Changchun University of Technology,2014,(02):143-147.
[15]王喜,赵宵凯,熊斌宇.一种基于数据挖掘技术的智能变电站故障诊断方法[J].陕西电力,2018,(04):39-43.WANG Xi,ZHAO Xiaokai,XIONG Binyu.Intelligent substation fault diagnosis method based on data mining technology[J].Shaanxi Electric Power,2018,(04):39-43.