Identification of critical contingencies using solution space pruning and intelligent search

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.