Direct power control of DFIG based wind turbine based on wind speed estimation and particle swarm optimization
This paper presents a direct power control (DPC) design of a grid connected doubly fed induction generator (DFIG) based wind turbine system in order to track maximum absorbable power in different wind speeds. A generalized regression neural network (GRNN) is used to estimate wind speed and thereby the maximum absorbable power is determined online as a function of wind speed. Finally the proposed DPC strategy employs a nonlinear robust sliding mode control (SMC) scheme to calculate the required rotor control voltage directly. The concept of sliding mode control is incorporated into particle swarm optimization (PSO) to determine inertial weights. The new DPC based on SMC-PSO scheme has acceptable harmonic spectra of stator current by using space vector modulation (SVM) block with constant switching frequency. Simulation results on 660-kw grid-connected DFIG are provided and show the effectiveness of the new technique, for tracking maximum power in presence machine parameters variation.
M. Tarafdar Hagh, S. Roozbehani, F. Najaty, S. Ghaemi, Y. Tan & K. M. Muttaqi, "Direct power control of DFIG based wind turbine based on wind speed estimation and particle swarm optimization," in Power Engineering Conference (AUPEC), 2015 Australasian Universities, 2015, pp. 1-6.