Composite Power System Vulnerability Evaluation to Cascading Failures Using Importance Sampling and Antithetic Variates

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 failure 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 to power generator outages. Another method combing importance sampling and antithetic variates together is implemented as well. It is shown that both methods noticeably reduce the number of samples that need to be investigated while maintaining the accuracy at a given level. It is found that the combined method outperforms importance sampling to certain extent. To illustrate the developed techniques, two case studies are conducted and analyzed on the IEEE one-area and three-area reliability test system.