Moving-horizon dynamic power system state estimation using semidefinite relaxation
Moving-horizon dynamic power system state estimation using semidefinite relaxation
复制标题
使用半定松弛的移动水平动态电力系统状态估计
DOI：
--
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
G. Giannakis
中科院分区：
文献类型：
--
作者：
G. Wang;Seung;G. Giannakis
准确的电力系统状态估计是保证电力系统可靠运行的重要前提。与静态PSSE不同，动态PSSE可以基于动态状态演化模型利用过去的测量，从而提供更高的准确性和状态可预测性。一个关键的挑战是非线性测量模型，尽管存在发散和局部最优问题，但通常使用线性化来解决。在这项工作中，提出了一种移动时域估计（MHE）策略，可以准确地捕获模型的非线性，具有很强的性能保证。为了减轻局部最优性，采用半定松弛方法，该方法通常提供接近全局最优的解决方案。数值试验表明，该方法可以显着改善扩展卡尔曼滤波（EKF）为基础的替代方案。
Accurate power system state estimation (PSSE) is an essential prerequisite for reliable operation of power systems. Different from static PSSE, dynamic PSSE can exploit past measurements based on a dynamical state evolution model, offering improved accuracy and state predictability. A key challenge is the nonlinear measurement model, which is often tackled using linearization, despite divergence and local optimality issues. In this work, a moving-horizon estimation (MHE) strategy is advocated, where model nonlinearity can be accurately captured with strong performance guarantees. To mitigate local optimality, a semidefinite relaxation approach is adopted, which often provides solutions close to the global optimum. Numerical tests show that the proposed method can markedly improve upon an extended Kalman filter (EKF)-based alternative.