This paper is interested in studying a system consisting of a wind turbine operating at variable wind speeds, and a two-feed asynchronous machine (DFIG) connected to the grid by a stator and fed by a transducer at the side of the rotor. The conductors are separately controlled for active and reactive power flow between the stator (DFIG) and the grid. The proposed controllers generate reference voltages for the rotor to ensure that the active and reactive power reaches the required reference values, to ensure effective tracking of the optimum operating point and to obtain the maximum electrical power output. Dynamic analysis of the system is performed under variable wind speeds. This analysis is based on active and reactive energy control. The new work in this paper is to introduce theories of genetic algorithms into the control strategy used in the switching chain of wind turbines in order to improve performance and efficiency. Simulation results applied to genetic algorithms give greater efficiency, impressive results, and stability to wind turbine systems are compared to classic PI regulators. Then, artificial intelligent controls, such as genetic algorithms control, are applied. Results obtained in the Matlab/Simulink environment show the efficiency of this proposed unit.

In this paper, a new modified model predictive control is proposed to improve the performance of the model predictive control for two-level voltage source inverters by alleviating computational burden and the disadvantages associated with the conventional model predictive control strategy. The objective of the proposed method is to reduce the number of candidate voltage vectors in each sector, thereby improving the overall performance of the control system, as well as achieving common-mode voltage reduction for two-level voltage source inverters. Two strategies are introduced to achieve this objective. Firstly, an algorithm is developed based on statistical computational processes to pre-define the candidate voltage vectors. This strategy involves ranking and considering the most frequently used voltage vectors. Consequently, by reducing the computational burden, the search space for the optimal voltage vectors is reduced. Furthermore, based on statistical results, a strategy is proposed to divide the sectors into three sectors instead of the six sectors in the conventional method. This approach effectively reduces the number of candidate voltage vectors. The modified model predictive control strategy aims to improve the efficiency of the control system by reducing the computational burden, and suppressing the common-mode voltage. The simulation and experiments are carried out to verify the effectiveness of the proposed strategy under various operational conditions. The results demonstrate that the modified model predictive control approach significantly reduces the computational burden and complexity of the control system while effectively suppressing the common-mode voltage; this contributes to improving the performance of two-level voltage source inverters and enhancing their applicability for connecting the renewable energies to the grid.

This paper presents a comprehensive study of a wind turbine system operating under variable wind conditions, utilizing a Doubly Fed Induction Generator (DFIG) connected to the grid. The DFIG is controlled via a rotor-side transducer, allowing for independent regulation of the conductors to manage both active and reactive power flows effectively. The control strategy focuses on generating reference voltages for the rotor to ensure that active and reactive power align with the desired targets, optimizing the tracking of the maximum power point to maximize electrical output. The research analyzes the system's dynamic performance under fluctuating wind conditions, emphasizing control strategies for managing active and reactive energy. A notable innovation is the integration of fuzzy logic and genetic algorithm into the control strategy for the wind turbine's switching mechanism, which enhances system performance and efficiency. Simulation results demonstrate that this approach provides higher efficiency, improved performance, and greater stability compared to the traditional Proportional-Integral (PI) controllers. Advanced artificial intelligence methods, such as fuzzy genetic algorithm control, were employed and the proposed system's effectiveness was validated with Matlab/Simulink simulations.