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Efficient Operation of Wind Turbine with Doubly Fed Induction Generator Using TLBO Algorithm and Artificial Neural Networks

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This paper investigates efficient operation of the doubly-fed induction generator (DFIG) considering iron losses with active power control capability for typical wind turbine (WT) system. The control objective is to determine the optimal rotor voltage to extract maximum active power from the DFIG over wide ranges of wind speed with two strategies: maximum efficiency strategy and stator UPF strategy. The Teaching Learning Based Optimization (TLBO) algorithm is used to achieve the above objective. The ideal power curve of a 2 MW wind turbine has been estimated to design the active power controller. Artificial neural network (ANN) controller is used as an adaptive dynamic controller to predict the value of rotor voltage for all operating points for the two case strategies. With the proposed control strategy, the DFIG-based WT provides maximum power point tracking (MPPT), fully power control with two control strategies. The overall WT system with DFIG iron losses consideration with the proposed controller is developed using Matlab/Simulink environment.
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Doubly Fed Induction Generator; Iron Losses; Maximum Efficiency; Unity Power Factor Operation; Active Power Control; Teaching Learning Based Optimization; Artificial Neural Network

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