Contingency assessment under uncertainty for voltage collapse and its application in risk based contingency ranking

Abstract

This paper proposes contingency assessment method that takes into account the nature of probability distribution of power system operating conditions to get realistic severity and risk estimations of contingencies. The uncertainty in operating conditions is captured by a mutually correlated non-parametric multivariate distribution, in addition to traditional standard distributions such as multivariate normal. While capturing these important features of operating conditions, the method also addresses reducing computational requirements by using linear sensitivities and machine learning techniques. The developed contingency assessment methods are applied on SEO region (Système Eléctrique Ouest, West France) of French EHV system to estimate severities and eventually rank the selected contingencies based on risk of voltage collapse.