Meta-Heuristic Approach for Validation and Calibration of Cascading Failure Analysis

A growing number of analytical and simulation methods are emerging for studying cascading failures, of which only a limited number are validated. In the cascading failure analysis, many parameters are involved, it is critical that these parameters are calibrated appropriately when the analysis is applied to a specific system. To propose a generic framework for the calibration and validation of cascading failure analysis, a multi-objective genetic algorithm based approach is developed. Its application is illustrated by the model parameters calibration of a AC PF cascading failure analysis. The parameters are optimized through minimizing the differences between the simulation results and the historical blackout data. The results for the case studies on the WECC network demonstrates the effectiveness of a meta-heuristic approach in calibrating the model parameters. The validation of the calibrated cascading failure analysis shows a good agreement between the simulation results and the historical data.