Numerical Solutions for Detecting Contingency in Modern Power Systems
Numerical Solutions for Detecting Contingency in Modern Power Systems
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DOI：
10.1109/ictc57116.2023.10154790
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Xiaohang Ma;Hongjiang Qian;L. Wang;M. Nazari;G. Yin
中科院分区：
文献类型：
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作者：
Xiaohang Ma;Hongjiang Qian;L. Wang;M. Nazari;G. Yin
本文致力于现代电力系统中的故障检测。由于我们考虑的系统是在网络物理系统的框架下，因此有必要考虑信息处理方面和通信网络。因此，噪声和随机干扰是不可避免的。然后，检测问题变成一个已知的最快检测。与在离散时间设置中运行检测问题导致一系列检测问题相反，这项工作侧重于在连续时间设置中的问题。我们对待随机微分方程模型。系统的一个显著特征是它是一个混合系统，既有连续状态，也有离散事件，它们共存并相互作用。离散事件过程用连续时间马尔可夫链来描述，表示不被连续样本路径表示的随机环境，最快检测问题可以写成最优停止问题。本文致力于寻找基本问题的数值解。我们使用马尔可夫链近似方法来构造数值算法。数值例子来证明性能。
This paper is devoted to the detection of contingencies in modern power systems. Because the systems we consider are under the framework of cyber-physical systems, it is necessary to take into consideration of the information processing aspect and communication networks. A consequence is that noise and random disturbances are unavoidable. The detection problem then becomes one known as quickest detection. In contrast to running the detection problem in a discrete-time setting leading to a sequence of detection problems, this work focuses on the problem in a continuous-time setup. We treat stochastic differential equation models. One of the distinct features is that the systems are hybrid involving both continuous states and discrete events that coexist and interact. The discrete event process is modeled by a continuous-time Markov chain representing random environments that are not resented by a continuous sample path. The quickest detection then can be written as an optimal stopping problem. This paper is devoted to finding numerical solutions to the underlying problem. We use a Markov chain approximation method to construct the numerical algorithms. Numerical examples are used to demonstrate the performance.