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The potential for cascading failure in power systems adds substantially to overall reliability risk. Monte Carlo sampling can be used with a power system model to estimate this impact, but doing so is computationally expensive. This paper presents a new approach to estimating the risk of large cascading blackouts triggered by multiple contingencies. The method uses a search algorithm (Random Chemistry)...
This paper presents a computationally efficient approach to estimate cascading failure risk in power systems. The method uses the previously published Random Chemistry algorithm [1] to find combinations of branch outages that lead to large blackouts, and then estimates risk by computing the expected blackout size based on the probabilities of various contingencies. We compare this method with Monte...
This paper describes a stochastic “Random Chemistry” (RC) algorithm to identify multiple (n-k) contingencies that initiate large cascading failures in a simulated power system. The method requires only O(log(n)) simulations per contingency identified, which is orders of magnitude faster than random search of this combinatorial space. We applied the method to a model of cascading failure in a power...
This paper describes two new approaches to cascading failure analysis in power systems that can combine large amounts of data about cascading blackouts to produce information about the ways that cascades may propagate. In the first, we evaluate methods for representing cascading failure information in the form of a graph. We refer to these graphs as "dual graphs" because the vertices are...
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