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Energies 2016, 9(6), 441; doi:10.3390/en9060441

Wind Turbine Driving a PM Synchronous Generator Using Novel Recurrent Chebyshev Neural Network Control with the Ideal Learning Rate

Department of Electrical Engineering, National Uinted University, Miaoli 36063, Taiwan
Academic Editor: Frede Blaabjerg
Received: 4 March 2016 / Revised: 24 May 2016 / Accepted: 3 June 2016 / Published: 9 June 2016
(This article belongs to the Collection Wind Turbines)
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Abstract

A permanent magnet (PM) synchronous generator system driven by wind turbine (WT), connected with smart grid via AC-DC converter and DC-AC converter, are controlled by the novel recurrent Chebyshev neural network (NN) and amended particle swarm optimization (PSO) to regulate output power and output voltage in two power converters in this study. Because a PM synchronous generator system driven by WT is an unknown non-linear and time-varying dynamic system, the on-line training novel recurrent Chebyshev NN control system is developed to regulate DC voltage of the AC-DC converter and AC voltage of the DC-AC converter connected with smart grid. Furthermore, the variable learning rate of the novel recurrent Chebyshev NN is regulated according to discrete-type Lyapunov function for improving the control performance and enhancing convergent speed. Finally, some experimental results are shown to verify the effectiveness of the proposed control method for a WT driving a PM synchronous generator system in smart grid. View Full-Text
Keywords: permanent magnet synchronous generator; wind turbine; recurrent Chebyshev neural network; discrete-type Lyapunov function permanent magnet synchronous generator; wind turbine; recurrent Chebyshev neural network; discrete-type Lyapunov function
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Lin, C.-H. Wind Turbine Driving a PM Synchronous Generator Using Novel Recurrent Chebyshev Neural Network Control with the Ideal Learning Rate. Energies 2016, 9, 441.

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