Reweighted Wirtinger Flow在电力系统状态估计问题中的应用  

Application of Reweighted Wirtinger Flow in Power System State Estimation Problem

作　　者：毛晓雨 

机构地区：[1]河北工业大学理学院,天津

出　　处：《应用数学进展》2025年第2期274-285,共12页Advances in Applied Mathematics

基　　金：河北省自然科学基金(项目编号:A2023202038)。

摘　　要：文章研究了Reweighted Wirtinger Flow (RWF)算法在基于二次测量的电力系统状态估计(PSSE)中的应用。PSSE是电力系统运行中的关键环节,它关系到电网的效率、可靠性和可持续性。传统的基于加权最小二乘(WLS)的高斯–牛顿迭代方法在处理非凸优化问题时容易陷入局部最优解。RWF算法在求解一般二次测量模型中被证明具有良好的性能,文章将其应用于PSSE问题,并通过对多个基准系统的数值实验,展示了RWF算法在计算速度和恢复精度方面的优势。实验结果表明,RWF算法在处理电力系统状态估计问题时,不仅提高了求解效率,还提升了恢复精度,尤其是在大规模系统中的应用潜力。此外,RWF算法在面对离群点时表现出更好的鲁棒性,这对于实际电力系统中的应用尤为重要。This paper investigates the application of the Reweighted Wirtinger Flow (RWF) algorithm in Power System State Estimation (PSSE) based on quadratic measurements. PSSE is a critical component in the operation of power systems, affecting the efficiency, reliability, and sustainability of the power grid. Traditional Weighted Least Squares (WLS), based on Gauss-Newton iterative methods, tend to get trapped in local optima when dealing with non-convex optimization problems. The RWF algorithm has been proven to perform well in solving general quadratic measurement models, and this paper applies it to the PSSE problem. Numerical experiments across multiple benchmark systems demonstrate the advantages of the RWF algorithm in terms of computational speed and estimation accuracy. The results indicate that the RWF algorithm not only improves solution efficiency but also enhances the accuracy of state estimation, especially for large-scale systems. Moreover, the RWF algorithm exhibits better robustness in the presence of outliers, which is particularly important for practical applications in power systems.

关 键 词：RWF算法 电力系统状态估计 二次测量 鲁棒性 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]