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Optimal control for variable-speed wind generation systems using General Regression Neural Network
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DOI：10.1016/j.ijepes.2014.02.015.png)
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An induction generator (IG) speed drive with the application of an optimal controller and a proposed General Regression Neural Network (GRNN) controller is introduced in this paper. Grid connected wind energy conversion system (WECS) present interesting control demands, due to the intrinsic nonlinear characteristic of wind mills and electric generators. The GRNN with adaptive ant colony optimization (AACO) torque compensation is feed-forward to increase the robustness of the wind driven induction generator system. An optimal control loop for the wind power system is designed. The optimality of the whole system is defined in relation with the trade-off between the wind energy conversion maximization and the minimization of the induction generator torque variation that is responsible for the frequency fluctuations. This is achieved by using a combined optimization criterion, resulting in a LQ tracking problem with an infinite horizon and a measurable exogenous variable (wind speed). The proposed controller is designed to drive the turbine speed to extract maximum power from the wind and adjust to the power regulation. (C) 2014 Elsevier Ltd. All rights reserved.
Keyword:
Optimal control
General Regression Neural Network (GRNN)
Adaptive ant colony optimization (AACO)
Wind turbine (WT)
Wind energy conversion system (WECS)
Induction generator (IG)
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