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Identification of critical contingencies using solution space pruning and intelligent search

机译：使用解决方案空间修剪和智能搜索来识别紧急情况

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The modern power grid is becoming vulnerable to a variety of cyber-physical attacks, and an intelligent attacker could choose an appropriate combination of components and trip them simultaneously to cause significant damage to the power grid. For instance, significant cascading failures may be caused. Such potential consequences call for a comprehensive examination of potential n - k contingencies. A main problem related to the n k analysis lies in the tremendous number of possible contingencies for a bulk power system. Thus, it is crucial to develop an efficient method to find the critical contingencies able to cause undesirable cascading failures. In this study, we develop a state space pruning based intelligent search method to identify these contingencies. First, the random chemistry method is deployed to sample a certain number of typical critical contingencies, which serve as a basis for the criticality analysis of components. Further, based on the criticality of components, the search space will be greatly reduced. Lastly, the particle swarm optimization algorithm is applied to search for the critical multiple contingencies. Case studies are conducted based on several IEEE bulk systems considering branch failures and bus failures, and the performance and efficiency of the proposed method is validated. The obtained collection of critical contingencies can provide useful information for making informed decisions to ensure secure power system operations. (C) 2017 Elsevier B.V. All rights reserved.
机译：现代电网正变得容易受到各种网络物理攻击的攻击，而聪明的攻击者可能会选择适当的组件组合并同时使它们跳闸，从而对电网造成重大破坏。例如，可能会导致严重的级联故障。这种潜在的后果要求对潜在的n-k突发事件进行全面检查。与n k分析有关的一个主要问题在于大功率系统可能发生的大量突发事件。因此，开发一种有效的方法以找到能够引起不希望的级联故障的紧急事件至关重要。在这项研究中，我们开发了一种基于状态空间修剪的智能搜索方法来识别这些意外情况。首先，采用随机化学方法对一定数量的典型临界意外事件进行采样，这是进行组件临界度分析的基础。此外，基于组件的重要性，搜索空间将大大减少。最后，应用粒子群优化算法搜索关键的多种突发事件。基于几个考虑分支故障和总线故障的IEEE批量系统进行了案例研究，并验证了所提方法的性能和效率。所获得的紧急事件的集合可以提供有用的信息，以进行明智的决策，以确保电力系统安全运行。 （C）2017 Elsevier B.V.保留所有权利。

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