Abstract
Recent large blackouts in power systems showed that common reliability criteria are not efficient anymore. Since cascading outages are the prevalent cause of more blackouts, and there is enormous interaction among different elements, determining the sequence of events before occurring blackouts is very challenging. Contingency screening methods are used to overcome some difficulties and identification of bottlenecks in power systems. However, risky contingencies are those with high probability, high consequences, or both. In this paper, a challenging work of seeking multiple events which potentially may result in cascades is addressed. Severe contingency identification, which is known as N-k problem in the literature, is difficult to deal with even for small values of k. A modified Binary Particle Swarm Optimization (BPSO), which proposed in this paper, could help power system planners and operators to upgrade the network resiliency by finding critical contingences that may initiate cascading outages. Severe contingencies are detected by computer simulations in the IEEE 39-bus test system and a real-sized network. The results are compared to the results of the IEEE 24-bus test system, which shows the method is more effective.
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