Heuristic optimization based approach for identification of power system dynamic equivalents

•A metaheuristic optimization based approach for identification of dynamic equivalents is proposed.•The approach bases on a new variant of the mean-variance mapping optimization (MVMO-SM).•Tests on the Ecuadorian–Colombian system demonstrates the applicability to real systems.•Comparisons with other optimization algorithms attest the outstanding performance of MVMO-SM.•Sensitivity analysis on MVMO-SM’s settings demonstrates its robustness. This paper introduces an approach for identification of dynamic equivalent parameters from measured operational dynamic responses associated to different disturbances. To tackle challenges related to optimization problem complexity (i.e., non-linearity of time-domain simulation based fitness calculation, discontinuity, non-convexity, and multimodality), the approach adopts a novel variant of the mean-variance mapping optimization algorithm to pursue efficient and fast search capability. This variant bases on swarm intelligence precepts and employs a multi-parent crossover criterion for offspring creation. Numerical tests performed on the Ecuadorian–Colombian interconnected system, including performance comparisons with other heuristic optimization tools, support the potential of the proposal to provide accurate estimates within a fast convergence rate.