The present research paper deals with a study of a variable speed wind energy conversion system (VSWECS) based on a Doubly Fed Induction Generator (DFIG), the stator is directly connected to the grid and driven by climbed back-to-back converters. Direct control of DFIG with a variable structure based on an artificial neural network is presented. Artificial Neuronal Network ANN is proposed to improve performances and to substitute the classical PI regulators in the direct control of active and reactive powers of the DFIG. The performance of the approach has been tested and validated by simulation for different operating conditions. Simulation results and improvement of the behavior of the DFIG are presented, using Matlab/Simulink software.
The present paper concerns the indirect control of the stator powers of a wind system based on a doubly-fed induction generator (DFIG). The DFIG is controlled by its rotor through an association of a grid side converter with a rotor side converter, and the stator makes it possible to supply a resistive load. The current 1.5 kw generator is driven by a wind emulator based on a DC motor. Simulation and experimental studies are carried out using, first, a conventional proportional integral, a neural network controller (NNC), and then a M5P decision tree algorithm (M5P-DTA) is proposed to bring improvements to the control. The M5P-DTA is obtained from a learning process via the dataset provided by NNCs. The proposed algorithm allows a fast and less complex control scheme for the DFIG. The simulation study and its results are obtained through the MATLAB/SIMULINK software, while the experimental test is carried out via the dSPACE DS1104 interface card ordered by MATLAB and the graphical interface of the Control-Desk software.
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