This paper proposes a novel swarm moth–flame optimizer (SMFO) to obtain the optimal parameters of four interacting proportional–integral (PI) loops of a doubly fed induction generator (DFIG)-based wind turbine, so that maximum power point tracking (MPPT) may be achieved together with an improved fault ride-through (FRT) capability. The SMFO is inspired by a moth swarm encircling a flame at night, in which each flame is simultaneously encircled by multiple moths for a greater exploitation, whereas the flame with a higher brightness (i.e. a smaller fitness function) will attract more moths than those of its adjacent flames. In order to achieve a wider exploration, a ring network is then constructed among the flames such that the moths may be guided to search for a brighter flame more effectively. Three case studies are undertaken, which verify that an improved global convergence, more optimal power tracking and enhanced FRT capability may be achieved by SMFO compared with those of existing meta-heuristic techniques.
Simulation of a Doubly Fed Induction Generator used in an Autonomous Variable Speed Hydro Turbine with Maximum Power Point Tracking Control
