Grouped grey wolf optimizer for maximum power point tracking of doubly-fed induction generator based wind turbine

Abstract This paper proposes a novel grouped grey wolf optimizer to obtain the optimal parameters of interactive proportional-integral controllers of doubly-fed induction generator based wind turbine, such that a maximum power point tracking can be realized together with an improved fault ride-through capability. Under the proposed framework, the grey wolves are divided into two independent groups, including a cooperative hunting group and a random scout group. The former one contains four types of grey wolves (i.e., alpha, beta, delta, and omega) to accomplish an effective hunting based on their hierachical cooperation and three elaborative maneuvers in the presence of an unknown environment, e.g., prey searching, prey encircling, and prey attacking, of which the number of beta and delta wolves is increased to achieve a deeper exploitation. On the other hand, the latter one undertakes a randomly global search and realizes an appropriate trade-off between the exploration and exploitation, thus a local optimum can be effectively avoided. Three case studies are carried out which verify that a better global convergence, more accurate power tracking and improved fault ride through capability can be achieved by the proposed approach compared with that of other heuristic algorithms.