摘要
电网设备参数的准确性是各种电网分析计算软件的基础。由于各种原因,线路及变压器的参数往往存在一些错误,从而影响到在线或离线计算程序的可信度。针对这一问题,本文基于多时段PMU或SCADA量测数据,进行了参数辨识和参数估计方法的研究。具体研究内容如下:
①充分考虑随机量测误差的正态分布特点,提出了分别基于单一设备多时段PMU和SCADA量测的两种错误参数均值辨识法。两种方法先是基于线路、双绕组和三绕组变压器的等值电路模型,分别建立PMU和SCADA量测信息的单一设备同一时段的综合标么值残差指标,以综合反映量测与参数误差对残差的影响;然后以方差系数为收敛条件,求取多时段综合残差代数和均值的绝对值(T指标),以突出参数误差对综合残差均值的影响;最后根据综合残差均值的大小来辨识设备的参数错误。论文分析了文献错误参数综合残差平方和均值辨识法存在的问题,并通过仿真分析验证了本文方法的有效性。
②针对参数估计值受量测误差影响而随机变化的问题,分别基于三绕组变压器三侧的PMU和SCADA多时段量测信息,提出了变压器电抗参数的两种抗差估计方法。两种方法先以变压器三侧的支路电流向量、中性点电压及电抗参数为状态变量,分别建立三绕组变压器的多时段PMU和SCADA量测方程及对应的增广最小二乘电抗参数估计模型,并采用高斯牛顿法求解。然后,以变压器电抗参数的最小二乘估计值为样本,以随机样本的方差系数门槛值为收敛判据,迭代计算变压器电抗参数最小二乘估计值的均值,并将其作为最终的参数估计值。考虑典型500kV三绕组变压器的不同负载水平,仿真分析了其最小二乘参数估计值的随机变化特点,同时验证了论文模型与方法的有效性。
The accuracy of grid equipment parameters is the base of the power system analyzing and computing softwares. For various reasons, there are always some errors for line and transformer parameters, thus affecting the reliability of on-line or off-line calculation program. Aiming at this problem, the paper carries on research to the method of parameter identification and estimation, based on multi-period PMU or SCADA data of single equipment. The contents presented in the paper are as follows:
Firstly, considering fully the Normal Distribution characteristic of random measurement error, the paper proposes two mean identification methods for parameter errors respectively based on multi-period PMU and SCADA data of single equipment. First, based on the equivalent circuit model of line, double winding transformer and three-winding transformer, and their PMU and SCADA measurement , the two methods respectively create comprehensive normalized value residual index of single equipment at the same period, which is to comprehensively reflect the influence of measurement and parameter errors on residual. Then convergence condition of variance coefficient is used to get the mean absolute value of algebraic sum of multi-period comprehensive residual (T index) ,in order to extrude the influence of parameter errors on comprehensive residual mean value. At last, according to the mean value of comprehensive residual, the parameter errors of the equipment can be identified. This paper analyzes the problem of the identification method in the present literature, which uses the style of the square sum mean value of comprehensive residual with parameter errors. At the same time, the effectiveness of the paper method is verified by simulation analysis.
Secondly, considering the problem of randomly changing parameter estimated values with the impact of measurement errors, this paper respectively presents two robust estimation methods of transformer reactance based on PMU and SCADA multi-period measurement information of three-winding transformer at three sides. First, regarded the transformer three side’s branch current phasor, neutral point voltage and reactance as state variables, the paper establishes three-winding transformer multi-period PMU or SCADA measurement equations and corresponding extended least squares reactance estimation model, and uses Gauss-Newton method to solve it. Second, regarded the least squares estimated values of transformer reactance as the sample, regarded the variance coefficient threshold of random sample as the convergence criterion, get the average value of the transformer reactance’s least squares estimated value by iterative calculation,and make it as the final parameter estimated values. Because of using the average estimation method and choosing the proper average samples, the proposed method has outstanding robust superiority. With the different load levels of typical 500kV three-winding transformer, this paper analyzes the random character of its least squares parameter estimated values, and also verifies the correctness of the paper model and method.
引文
[1]马志强.电力系统状态估计中的参数识别与结线识别[J].电力系统自动化, 1980, 1(3):12-26.
[2]王斌.电力系统状态估计中的参数估计研究[D].河海大学, 2007, 3.
[3] Th.Van Cutsem,V.H.Quintana. Network parameter estimation using online data with application to transformer tap position estimation [J].IEE Proceedings-Part C,1988,135(1):31-40.
[4] Wen-Hsiung E.Liu,Felix F.wu,shau-Ming Lun. Estimations of parameter errors from measurement residuals in state estimation [J]. IEEE Trans on Power Systems,1992,7 (1):81-89.
[5] W.-H.Edwin Liu, Swee-Lian Lim. Parameter error identification and estimation in power system state estimation [J]. IEEE Trans on Power Systems, 1995, 10 (1):200-209.
[6]杨滢,孙宏斌,张伯明,张海波.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(4):43-49
[7] Jun Zhu, Ali Abur. Identification of network parameter errors [J]. IEEE Transactions on Power Systems, 2006,21(2):586-592.
[8]周苏荃,徐艳,张艳军.电力网络支路参数估计新方法[J].电力系统自动化, 2009, 21(2): 11-14.
[9]陈晓刚,易永辉,江全元,等.基于WAMS_SCADA混合量测的电网参数辨识与估计[J].电力系统自动化,2008,32(5):5-9.
[10] Pedro Zarco, Antonio Gomez Exposito. Power system parameter estimation: a survey [J]. IEEE Transactions on power system,2000,15(1):216-222.
[11]李碧君,薛禹胜,顾锦汶,等.电力系统状态估计问题的研究现状和展望[J].电力系统自动化,1998,22(11):53-60.
[12]于尔铿.电力系统状态估计[M].北京:水利电力出版社,1985.
[13] Debs, A.S. Estimation of steady-state power system model parameters[J]. IEEE Transactions on Power Apparatus and Systems, 1974,93(5): 1260-1268.
[14] E.Handschin, E.Kliokys. Transformer tap position estimation and bad data detection using dynamic signal modelling[J]. IEEE Transactions on Power Systems,1995,10(2):810-817.
[15]王兴,刘广一,于尔铿.基于变比变化检测的变压器抽头位置跟踪估计算法[J].中国电机工程学报,1997,17(3):162-165.
[16] Ilya W. Slutsker, Kevin A. Clements. Real time recursive parameter estimation in energy management systems[J]. IEEE Transactions on power system,1996,11(3):1393-1399
[17]郭金莲.多区域电力系统分布式状态估计算法的研究[D].华北电力大学, 2008, 12.
[18] M.Allam and M.Laughton, Ageneral algorithm for estimation power system variables and network parameters[C]. IEEE PES 1974 Summer Meeting,Anaheim, CA,1974, Paper C74 331-5.
[19]——,Static and dynamic algorithms for power system variable and parameter estimation[C], Proceedings 5th Power System Computation Conference, United Kingdom, Sept.1975, Paper 2.3/11
[20] P.Teixeira, S.Brammer, W.Rutz, et al, State estimation of voltage and phase-shift transformer tap settings[J], IEEE Trans on Power System, vol.7, no.3, pp.1386-1393, Aug.1992.
[21] A.Reig, C.Alvarez, Off-line parameter estimation techniques for network model data tuning[C], Proceedings TASTED Power High Tech’89, Valencia, Spain,1989, pp. 205-210.
[22] P.Zarco, A.Gomez, Off-line determination of network parameters in state estimation[C], Proceedings 12th Power System Computation Conference, Dresden, Germany, Aug. 1996, pp.1207-1213.
[23]杨滢.拓扑错误辨识与参数估计的理论分析和算法研究[D].清华大学,2005,6.
[24] W.Kotiuga, M.Vidyasagar, Bad data rejection properties of weighted least absolute value techniques applied to static state estimation[J], IEEE Trans. PAS-101, No.4, April 1982, pp.844-854.
[25]何桦,张瑜,宣丽娜.基于线性内点法及正交变换的抗差参数估计[J].电力系统自动化,2007,31(20):36-40.
[26]李凌.基于现代内点理论的电力系统加权非线性L_1范数状态及参数估计研究[D].广西大学,2002,5.
[27]程彤,颜伟,文雨,等.基于变压器参数修正的变电站状态估计[J].重庆大学学报(自然科学版), 2006,29(3):32-35.
[28]王茂海,鲍捷,张哲.基于PMU实测数据的输电线路参数在线估计方法[J].电力系统自动化,2010,34(1):25-27.
[29] D.Fletcher, W.stadlin. Transformer tap position estimation[J].IEEE Transactions on Power Systems, 1983,102(11):3680-3686.
[30]何桦,柴京慧,卫志农,等.基于量测残差的改进参数估计[J].电力系统自动化, 2007,31(4):33-36.
[31]李大路,李蕊,孙元章,等.计及广域测量信息的状态估计错误参数识别与修正[J].电力系统自动化,2008,32(14):11-15.
[32]柴京慧,李书敏,何桦.基于PMU及多时间断面的输电网参数估计[J].电力系统自动化,2009,33(11):49-52.
[33]陈晓刚.电网广域安全监测系统若干关键技术问题研究[D].浙江大学.2008,12
[34]周琦.基于PMU优化的电力系统状态估计算法的研究[D].华北电力大学,2008,12.
[35] Naim Logic. The impact of parameters errors on power system state estimation[D]. ARIZONA STATE UNIVERSITY. 2004 August.
[36]诸骏伟.电力系统分析[M].北京:中国电力出版社,1995.
[37]于尔铿.电力系统状态估计[M].北京:水利电力出版社,1985.
[38]肖刚,李天柁,系统可靠性分析中的蒙特卡罗方法[M].科技出版社,2003.
[39]李文沅.电力系统风险评估——模型、方法和应用[M].北京:科学出版社,2006.
[40]费业泰.误差理论的研究与进展[J].计量技术,1998,4(8):40-41.
[41]国家电力调度通信中心.电力系统实时动态监测系统技术规范:试行[EB/OL]. http://www.docin.com/p55664542.html, [2010-4-2].
[42]张旭俊,谢美萍.三相电压不平衡度的计算图[J].华中电力,1996,9(1):54-58.
[43]郭峰,姚莉娜,刘恒,等.引入三相不平衡度的低压电网理论线损计算[J].电力自动化设备,2007,27(11):51-54.
