The distribution power subsystems are usually interconnected to each other, so the design of the interconnected optimal filtering algorithm for distributed state estimation is a challenging task. Driven by this motivation, this paper proposes a novel consensus filter based dynamic state estimation algorithm with its convergence analysis for modern power systems. The novelty of the scheme is that the algorithm is designed based on the mean squared error and semidefinite programming approaches. Specifically, the optimal local gain is computed after minimizing the mean squared error between the true and estimated states. The consensus gain is determined by a convex optimization process with a given suboptimal local gain. Furthermore, the convergence of the proposed scheme is analyzed after stacking all the estimation error dynamics. The Laplacian operator is used to represent the interconnected filter structure as a compact error dynamic for deriving the convergence condition of the algorithm. The developed approach is verified by using the renewable microgrid. It shows that the distributed scheme being explored is effective as it takes only 0.00004 seconds to properly estimate the system states and does not need to transmit the remote sensing signals to the central estimator.

中文翻译：

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基于共识的智能电网状态估计算法


分布式电源子系统通常相互互连，因此用于分布式状态估计的互连最优滤波算法的设计是一项具有挑战性的任务。受此动机的驱动，本文提出了一种新颖的基于一致性滤波器的动态状态估计算法及其针对现代电力系统的收敛性分析。该方案的新颖之处在于算法是基于均方误差和半定规划方法设计的。具体来说，在最小化真实状态和估计状态之间的均方误差后计算最佳局部增益。共识增益由具有给定次优局部增益的凸优化过程确定。此外，在叠加所有估计误差动态后，分析了所提出方案的收敛性。拉普拉斯算子用于将互连滤波器结构表示为紧凑的误差动态，以推导算法的收敛条件。所开发的方法通过使用可再生微电网进行了验证。结果表明，所探索的分布式方案是有效的，因为只需 0.00004 秒即可正确估计系统状态，并且不需要将遥感信号传输到中央估计器。