Evaluation of ANN Estimation-Based MPPT Control for a DFIG Wind Turbine

Authors

Chun Wei, University of Nebraska-LincolnFollow
Liyan Qu, University of Nebraska-LincolnFollow
Wei Qiao, University of Nebraska–LincolnFollow

Date of this Version

2014

Citation

Power Electronics and Machines for Wind and Water Applications (PEMWA), 2014 IEEE Symposium

Comments

©2014 IEEE

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.