Optimal maximum power point tracking of wind turbine doubly fed induction generator based on driving training algorithm

Abstract

The operation of wind power system at optimum power point is a big challenge particularly under uncertainty of wind speed. As a result, it is necessary to install an effective maximum power point tracking (MPPT) controller for extracting the available maximal power from wind energy conversion system (WECS). Therefore, this paper aims to obtain the optimal values of injected rotor phase voltage for doubly fed induction generator (DFIG) to ensure the extraction of peak power from wind turbine under different wind speeds as well as to get the optimal performance of DFIG. A new metaheuristic optimization approach; Driving Training Algorithm (DTA) is used to crop the optimal DFIG rotor voltages. Three different scenarios are presented to have MPPT, the first one is the MPPT with unity stator power factor, the second one is the MPPT with minimum DFIG losses, and the third scenario is MPPT with minimum rotor current to reduce the rating of rotor inverter. The MATLAB environment is used to simulate and study the proposed controller with 2.4 MW wind turbine. The optimum power curve of wind turbine has been estimated to get the reference values of DFIG mechanical power. The results ensured the significance and robust of the proposed controller to have MPPT under different wind speeds. The DTA results are compared with other two well-known optimization algorithms; water cycle algorithm (WCA) and particle swarm optimizer (PSO) to verify the accuracy of results.