Optimized control of DFIG based wind generation using swarm intelligence

In this paper, a particle swarm optimization with ε-greedy (ePSO) algorithm and group search optimizer (GSO) algorithm are compared with the classic PSO algorithm for the optimal control of DFIG wind generation based on small signal stability analysis (SSSA). In the modified ePSO algorithm, the cooperative learning principle among particles has been introduced, namely, particles not only adjust its own flying speed according to itself and the best individual of the swarm but also learn from other best particles according to certain probability. The proposed ePSO algorithm has been tested on benchmark functions and demonstrated its effectiveness in high-dimension multi-modal optimization. Then ePSO is employed to tune the controller parameters of DFIG based wind generation. Results obtained by ePSO are compared with classic PSO and GSO, demonstrating the improved performance of the proposed ePSO algorithm.