Evaluation of ANN estimation-based MPPT control for a DFIG wind turbine

Abstract

This paper proposes an artificial neuronal network (ANN) estimation-based wind speed sensolress MPPT algorithm for wind turbines equipped with doubly-fed induction generators (DFIG). The ANN is designed to produce the optimal control signal for the DFIG power or speed controller. The optimal parameters of the ANN are determined by using a particle swarm optimization (PSO) algorithm. A 3.6 MW DFIG wind turbine is simulated in PSCAD to evaluate and compare the proposed MPPT method with the traditional tip speed ratio (TSR) and turbine power profile-based MPPT methods in both the speed control and power control modes in variable wind speed conditions.