Title: Optimal active and reactive power control of wind turbine driven DFIG using TLBO algorithm and artificial neural networks

Authors: Mahmoud M. ElKholy; H.M.B. Metwally; Garib M. Regal; M. Ali Sadek

Addresses: Electrical Power and Machines Engineering Department, Faculty of Engineering, Zagazig University, Zagazig, Egypt ' Electrical Power and Machines Engineering Department, Faculty of Engineering, Zagazig University, Zagazig, Egypt ' Electrical Power and Machines Engineering Department, Faculty of Engineering, Zagazig University, Zagazig, Egypt ' Electrical Power and Machines Engineering Department, Faculty of Engineering, Zagazig University, Zagazig, Egypt

Abstract: This paper investigates the optimal active and reactive power control capabilities for typical wind turbine (WT) driven doubly fed induction generator (DFIG). The main objective is to determine the optimal rotor voltage to extract certain active and reactive power from the DFIG over wide ranges of wind speed. Teaching learning-based optimisation (TLBO) algorithm is a new heuristic optimisation technique, used to obtain the optimum rotor voltages to achieve reference active and reactive powers overall operating points. Artificial neural network (ANN) controller is used as an adaptive controller to predict the value of rotor voltage for all operating points. The ideal power curve of a 2 MW wind turbine has been estimated to design the active power controller. The stator reactive power control capability with the range of ±1.6 MVAR is developed. With the proposed control strategy, the DFIG-based wind farm provides maximum power point tracking (MPPT), fully active and reactive powers control. For all operated wind speeds, the adaptive proposed controller develops useful network support compared to the conventional DFIG-based wind farm. The proposed system is developed in MATLAB/Simulink environment.

Keywords: doubly fed induction generator; DFIG; active and reactive power control; teaching learning-based optimisation; TLBO; artificial neural network; ANN.

DOI: 10.1504/IJRET.2017.086810

International Journal of Renewable Energy Technology, 2017 Vol.8 No.2, pp.132 - 153

Received: 25 Apr 2016
Accepted: 15 Dec 2016
Published online: 28 Sep 2017 *

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