ABSTRACT
Cascading failures events are major concerns for future power grids and are generally not treatable analytically. For realistic analysis of the cascading sequence, dedicated models for the numerical simulation are often required. These are generally computationally costly and involve many parameters and variables. Due to uncertainty associated with the cascading failures and limited or unavailable historical data on large size cascading events, several factors turn out to be poorly estimated or subjectively defined. In order to improve confidence in the model, sensitivity analysis is applied to reveal which among the uncertain factors have the highest influence on a realistic DC overload cascading model. The 95th percentile of the demand not served, the estimated mean number of line failures and the frequency of line failure are the considered outputs. Those are obtained by evaluating random contingency and load scenarios for the network. The approach allows to reduce the dimensionality of the model input space and to identifying inputs interactions which are affecting the most statistical indicators of the demand not supplied.
