Identification of Nonlinear Synchronous Generator Parameters Using Stochastic Fractal Search Algorithm

This paper develops a general output error identification approach of nonlinear synchronous generator parameters. The considered method is based on a meta-heuristic optimization algorithm called stochastic fractal search (SFS). It consists of minimizing a quadratic criterion that represents the squared difference between the simulated model outputs and those obtained from the model to be identified by using the SFS algorithm. To highlight the performance of the proposed method, a deep comparison with the PSO algorithm was carried out. The obtained results proved the superiority of the proposed approach in terms of stability, robustness, estimation accuracy and convergence speed.