论文标题
通过降低模型阶的分布网格的加速概率状态估计
Accelerated Probabilistic State Estimation in Distribution Grids via Model Order Reduction
论文作者
论文摘要
本文将自定义模型减少技术应用于分布网格状态估计问题。具体而言，该方法针对的是，由于伪测量的不确定性，可以比采样的输入扰动以计算基础系统状态的概率界限，这是有利的。该例程称为加速概率状态估计量（APSE），有效地搜索具有降低订单模型（ROM）的低维子空间中的顺序状态估计问题的解决方案。当找不到足够精确的溶液时，APSE会恢复为基于QR分解的高斯 - 耐用求解器。然后，它使用所得解决方案来预形成低维子空间的简单基础扩展，从而改善了降低的模型求解器。从不平衡的三相8500节点分布网格收集的模拟测试结果显示，结果算法几乎比可比的全阶高斯 - 纽顿求解器快几乎要快的数量级，因此可能足够快地实时使用。
This paper applies a custom model order reduction technique to the distribution grid state estimation problem. Specifically, the method targets the situation where, due to pseudo-measurement uncertainty, it is advantageous to run the state estimation solver potentially thousands of times over sampled input perturbations in order to compute probabilistic bounds on the underlying system state. This routine, termed the Accelerated Probabilistic State Estimator (APSE), efficiently searches for the solutions of sequential state estimation problems in a low dimensional subspace with a reduced order model (ROM). When a sufficiently accurate solution is not found, the APSE reverts to a conventional QR factorization-based Gauss-Newton solver. It then uses the resulting solution to preform a simple basis expansion of the low-dimensional subspace, thus improving the reduced model solver. Simulated test results, collected from the unbalanced three-phase 8500-node distribution grid, show the resulting algorithm to be almost an order of magnitude faster than a comparable full-order Gauss-Newton solver and thus potentially fast enough for real-time use.
