基于正交变换与置信域的量测方差估计与权重设置算法

在状态估计实际应用中,量测方差获取和权重设置存在一定的困难。伴随状态估计运算量越来越繁重,现有量测方差估计算法的收敛性无法得到保证。为此提出了一种基于正交变换与置信域的量测方差估计和权重设置算法。利用正交变换降低迭代系数矩阵条件数,提高量测方差估计的数值稳定性;根据正交变换的化简结果,结合拉格朗日乘子法,建立新的量测方差估计模型;对于迭代过程的中间数据,采用置信域作为目标函数的约束,确保每次估计结果落在置信区间内;最后,通过IEEE 14/30/118标准系统算例证明了该算法的有效性。

Measurement Variance Estimation and Weights Configuration Algorithm Based on Orthogonal Transformation and Trust Region

In the practical applications of state estimation, it is difficult to obtain the measurement variances and set the weights of measurements. With the computation of state estimation getting harder, the convergence of the existing measurement variance estimation algorithm can not be guaranteed. To solve this problem, an algorithm based on orthogonal transformation and trust region to estimate measurement variance and set weight is proposed in this paper. The numerical stability of measurement variance estimation is improved by the orthogonal transformation method, which can reduce the condition number of the coefficient matrix at each iteration. According to the reduced results of orthogonal transformation, combining with Lagrange multipliers method, a new measurement variance estimation model is established. The trust region is considered as the constraint of the objective function to ensure that each estimation result data is in a confidence interval. The simulation results of the IEEE 14 bus system, IEEE 30 bus system and IEEE 118 bus system show the effectiveness of the proposed algorithm.