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Methodology for multiarea state estimation solved by a decomposition method

机译：分解方法求解的多区域状态估计方法

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As power systems are large interconnected systems with a high degree of complexity, the control and operation of such systems become a challenging task. Thus, large-scale power systems are mostly operated as interconnected subsystems. In this paper, the state estimation problem is addressed through a decentralized optimization scheme with minimum information exchange among subsystems. This paper focuses on a methodology for solving the multiarea state estimation problem by a decomposition method. This method is derived from the Lagrangian relaxation method and is named optimality condition decomposition (OCD). Results are presented for the IEEE 118-buses test power system, which has been split into two and three subsystems. (C) 2015 Elsevier B.V. All rights reserved.
机译：由于电力系统是具有高度复杂性的大型互连系统，因此此类系统的控制和操作成为一项艰巨的任务。因此，大型电力系统主要作为互连的子系统运行。在本文中，状态估计问题是通过分散优化方案解决的，子系统之间的信息交换最少。本文着重介绍一种通过分解方法解决多区域状态估计问题的方法。该方法源自拉格朗日松弛法，称为最优性条件分解（OCD）。给出了IEEE 118总线测试电源系统的结果，该系统已分为两个和三个子系统。 （C）2015 Elsevier B.V.保留所有权利。

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