Stochastic Uncertainty Propagation in Power System Dynamics Using Measure-Valued Proximal Recursions
Stochastic Uncertainty Propagation in Power System Dynamics Using Measure-Valued Proximal Recursions
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DOI：
10.1109/tpwrs.2022.3217267
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发表时间：
2021-08
影响因子：
6.6
通讯作者：
A. Halder;Kenneth F. Caluya;Pegah Ojaghi;Xinbo Geng
中科院分区：
文献类型：
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作者：
A. Halder;Kenneth F. Caluya;Pegah Ojaghi;Xinbo Geng
我们提出了一种近似算法，该算法在联合概率测度空间上进行变分递归，以在高维状态空间上传播电力系统动态中的随机不确定性。该算法充分利用了网络化电力系统暂态过程中的精确非线性结构，具有非参数特性。提升的动态空间的概率措施，使我们能够设计一个可扩展的算法，避免网格化的基础高维状态空间，这是计算上的禁止。近端递归实现了广义无穷维梯度流，并演化概率加权的散乱点云。我们澄清的理论细微差别和算法的细节，具体到电力系统的非线性，并提供说明性的数值例子。
We present a proximal algorithm that performs a variational recursion on the space of joint probability measures to propagate the stochastic uncertainties in power system dynamics over high dimensional state space. The proposed algorithm takes advantage of the exact nonlinearity structures in the trajectory-level dynamics of the networked power systems, and is nonparametric. Lifting the dynamics to the space of probability measures allows us to design a scalable algorithm that obviates gridding the underlying high dimensional state space which is computationally prohibitive. The proximal recursion implements a generalized infinite dimensional gradient flow, and evolves probability-weighted scattered point clouds. We clarify the theoretical nuances and algorithmic details specific to the power system nonlinearities, and provide illustrative numerical examples.