Development of a Multiple Model Predictive Control for DFIG Based Wind Energy Conversion System
Abstract
This work developed a multiple input multiple output model predictive control (MMPC) scheme based for a grid connected wind turbine system with a view to extracting the maximum power from a doubly fed induction generator (DFIG) under an unbalanced condition. The work employed a MMPC scheme for controlling the generator torque and pitch angle simultaneously, so as to reduces the mechanical stress, flicker emission, drive train load and effectively exploit the advantage of high penetration of wind farm. The control strategy was formulated for the whole operating region of the wind turbine system both low and high-speed regime. In addition, multiple model predictive control comprising different MPC was designed based on the operating wind speed. A baseline controller using gain scheduled proportional-integral controller was implemented on a GE 1.5 MW Wind turbine system is used to test the effectiveness of the developed controller. Based on results obtained a reduction in 11.04% and 22.42% in flicker emission and drive train load was obtained for the MMPC as compared to gain scheduled PI controller of 0.540752 and 0.216369 respectively at low speed regime (6m/s). Whilst at high speed regime (16m/s) the MMPC recorded a reduction in flicker emission and drive train load of 65.36% and 65.21% respectively as compared to gain scheduled PI controller of value 0.032236, 0.032236 respectively. The performance of the MMPC outperforms the standard baseline in tracking the desire set points using realistic wind speed model with a reduction in both flicker emission and drive train load.
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