Risk-based composite power system vulnerability evaluation to cascading failures using importance sampling

Abstract

Large-scale blackouts typically result from cascading failure in power systems operation. Their mitigation in power system planning calls for the development of methods and algorithms that assess the risk of cascading failures due to relay overtripping, short-circuits induced by overgrown vegetation, voltage sags, line and transformer overloading, transient instabilities, voltage collapse, to cite a few. This paper describes such a method based on composite power system reliability evaluation via sequential Monte Carlo simulation. One of the impediments of the study of these phenomena is the prohibitively large computational burden involved by the simulations. To overcome this difficulty, importance sampling technique utilizing the Weibull distribution is applied. It is shown that the method significantly reduce the number of samples that need to be investigated while maintaining the accuracy at a given level. To illustrate the developed approach, a case study is conducted and analyzed on the IEEE reliability test system.

Efficient simulation of blackout probabilities using splitting

“…First, line contact with vegetation is inherently stochastic: Vertical sag depends on stochastic factors such as wind and ambient temperature, and vertical vegetation growth can also be considered stochastic (e.g., [8]). Second, protective devices, which may operate correctly or incorrectly, can be a source of randomness in line capacity (e.g., [2,7], use hidden failures of protective relays as a basis for cascading blackout models).…”

Statistical Properties and Classification of N-2 Contingencies in Large Scale Power Grids

“…The statistical properties of the N − 2 contingencies may be used to bias the distribution of starting configurations of cascade simulation software based on more realistic models of power grid dynamics. This way faster convergence of the Monte Carlo based algorithms may be achieved [20]. It may also help improve various heuristics that were proposed for contingency selection problems.…”

Mutual information based Bayesian analysis of power system reliability

“…The final step in Bayesian analysis is creating the Bayesian network. After considering the rare events through importance sampling, mutual information [24] is used to ascertain the stronger dependencies between nodes. It is a simple and natural measure of dependency.…”