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A distributed Gauss-Newton method for power system state estimation

机译:电力系统状态估计的分布式高斯-牛顿法

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We propose a fully distributed Gauss-Newton algorithm for state estimation of electric power systems. At each Gauss-Newton iteration, matrix-splitting techniques are utilized to carry out the matrix inversion needed for calculating the Gauss-Newton step in a distributed fashion. In order to reduce the communication burden as well as increase robustness of state estimation, the proposed distributed scheme relies only on local information and a limited amount of information from neighboring areas. The matrix-splitting scheme is designed to calculate the Gauss Newton step with exponential convergence speed. The effectiveness of the method is demonstrated in various numerical experiments.
机译:我们提出了一种用于电力系统状态估计的完全分布式高斯-牛顿算法。在每个高斯-牛顿迭代中,都采用矩阵分解技术来进行以分布方式计算高斯-牛顿步长所需的矩阵求逆。为了减轻通信负担并提高状态估计的鲁棒性,提出的分布式方案仅依赖于本地信息和来自相邻区域的有限数量的信息。矩阵分解方案设计为以指数收敛速度计算高斯牛顿阶跃。在各种数值实验中证明了该方法的有效性。

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