Observability Analysis of a Power System Stochastic Dynamic Model Using a Derivative-Free Approach
Observability Analysis of a Power System Stochastic Dynamic Model Using a Derivative-Free Approach
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DOI：
10.1109/tpwrs.2021.3079919
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发表时间：
2021-11-01
影响因子：
6.6
通讯作者：
Wang, Yuhong
中科院分区：
文献类型：
--
作者：
Zheng, Zongsheng;Xu, Yijun;Wang, Yuhong
作为电力系统动态状态估计的前提，电力系统动态模型的可观测性分析近年来引起了许多电力工程师的关注。然而，由于该模型是典型的非线性和大规模的，其可观性的分析是一个挑战，传统的基于导数的方法。实际上，基于线性近似的方法可能提供不可靠的结果，而基于非线性技术的方法不可避免地面临极其复杂的推导。此外，由于电力系统本质上是随机的，传统的确定性方法可能会导致不准确的可观性分析。面对这些挑战，我们提出了一种新的基于多项式混沌的无导数可观测性分析方法，不仅是免费的任何线性近似，但也占了随机性的动态模型，同时带来了低的实现复杂度。此外，这种方法使我们能够量化随机模型的可观测性程度，这是传统的确定性方法无法做到的。所提出的方法的优良性能已被证明通过使用IEEE-DC 1A励磁机和TGOV 1涡轮机调速器的同步发电机模型进行广泛的仿真。
Serving as a prerequisite to power system dynamic state estimation, the observability analysis of a power system dynamic model has recently attracted the attention of many power engineers. However, because this model is typically nonlinear and large-scale, the analysis of its observability is a challenge to the traditional derivative-based methods. Indeed, the linear-approximation-based approach may provide unreliable results while the nonlinear-technique-based approach inevitably faces extremely complicated derivations. Furthermore, because power systems are intrinsically stochastic, the traditional deterministic approaches may lead to inaccurate observability analyses. Facing these challenges, we propose a novel polynomial-chaos-based derivative-free observability analysis approach that not only is free of any linear approximations, but also accounts for the stochasticity of the dynamic model while bringing a low implementation complexity. Furthermore, this approach enables us to quantify the degree of observability of a stochastic model, what conventional deterministic methods cannot do. The excellent performance of the proposed method has been demonstrated by performing extensive simulations using a synchronous generator model with IEEE-DC1A exciter and the TGOV1 turbine governor.