Title: Maximum power production operation of doubly fed induction generator wind turbine using adaptive neural network and conventional controllers

Authors: Hazem Hassan Ali; Ghada Saeed El Basuony; Nashwa Ahmad Kamal

Addresses: Electrical Engineering Department, Marg High Institute for Engineering and Modern Technology, Cairo, Egypt ' Electrical Engineering Department, Cairo University, Cairo, Giza, Egypt ' Electrical Engineering Department, Cairo University, Cairo, Giza, Egypt

Abstract: Production of maximum power based on control of the Rotor Side Converter (RSC) of Doubly Fed Induction Generator (DFIG) wind turbine direct axis current is necessary to accomplish fast reaching to the maximum power point and protect the working parts in RSC from high overshoot in the current. An assessment study between adaptive Neural Network (NN) and conventional Proportional Integral (PI) controllers for control of the RSC direct axis current is introduced in this study. NN controller-based Levenberg-Marquardt backpropagation (LMBP) is designed and is trained to mainly control RSC direct axis current. Also, RSC direct axis current is extracted based on PI controller which used to control the speed of DFIG according to the optimum tip speed ratio obtained by genetic algorithm. The simulation results show that the RSC based NN controller is better than RSC-based conventional speed regulator in protecting RSC parts from high overshoot in the current.

Keywords: DFIG wind turbine; MPPT; GA technique; NN controller; PI controller.

DOI: 10.1504/IJCAT.2021.114984

International Journal of Computer Applications in Technology, 2021 Vol.65 No.2, pp.173 - 187

Received: 16 Jun 2020
Accepted: 26 Jul 2020
Published online: 13 May 2021 *

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