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Bilinear WLAV power system state estimation based on interior point method

机译:基于内点法的双线性WLAV电力系统状态估计

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摘要

Traditional weighted least absolute value (WLAV) rubust state estimation based on interior point method (IPM) can improve the estimation accuracy by minimizing the impact of bad datas which is limited in practical application because of low efficiency. In this paper, a bilinear weighted least absolute value (WLAV) power system state estimation (SE) based on interior point method is proposed, where the introducing of intermediate variables transfers the nonlinear measurements functions into two linear equations with two nonlinear transformation in between. The computation of Hessian matrix in correction equation is avoided and the dimention of coefficient matrix in Karush-Kuhn-Tucker(KKT) condition is reduced when applying the interior point algrithm to solve the linear equations. Finally, compared with the traditional method, it follows from the simulation results that the proposed method possesses better computational efficiency and estimating precision.
机译:传统的基于内点法(IPM)的加权最小绝对值(WLAV)鲁棒状态估计可以通过最大程度地降低不良数据的影响来提高估计精度,这在实际应用中由于效率低而受到限制。本文提出了一种基于内点法的双线性加权最小绝对值(WLAV)电力系统状态估计(SE),其中引入中间变量将非线性测量函数转换为两个线性方程,并且在两个线性方程之间进行了两次非线性变换。应用内点算法求解线性方程组时,避免了校正方程中Hessian矩阵的计算,并减少了Karush-Kuhn-Tucker(KKT)条件下系数矩阵的维数。最后,与传统方法相比,从仿真结果可以看出,该方法具有较高的计算效率和估计精度。

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