基于测量不确定度的电力系统状态估计 (三)算法比较

Power System Static-state Estimation Based on Uncertainty of Measurement Part Three Algorithms Compared

加权最小二乘估计准则在电力系统状态估计中应用广泛,但其结果易受不良测点影响。为解决这一问题,已经提出了加权最小绝对值准则、非二次准则等估计器。应用大量算例,对已有估计准则和文中所提出的以最大测点正常率为目标的状态估计方法进行了对比研究,进一步对残差污染情况进行了讨论。算例表明,在估计结果合理性方面,当测点中不含不良数据时,文中所提出的方法与加权最小二乘估计相当,优于其他方法;当测点中含有不良数据时,文中所提出的方法大幅度优于以往状态估计方法,说明所提出的方法可较好地解决残差污染问题。

The weighted least squares （WLS） estimator is the currently most widely used method for power system state estimation. However, the estimation results of WLS could be far from the true values of state variables when bad data, are present. To solve the problem, a non-quadratic objective estimator, the weighted least absolute value （WLAV） estimator, and least mean squares （LMS） estimator are proposed. Numerical simulations, including residual pollution examples, are carried out to compare existing estimators and the maximum normal rate estimator （MNR） described in the second paper of this series. Test results show that the proposed method is as good as WLS and better than other methods when no bad data is contained in the test system, and it is far superior to all other methods when test points contain bad data, showing that the proposed method can deal with the residual pollution scenario fairly well.