Maximum power point tracking (MPPT) techniques for wind turbines have a significant effect on renewable energy production. Therefore, the association of the indirect torque control of the switched reluctance generator (SRG) with the wind turbine considering the MPPT technique has been developed in this work. The proposed strategy has a great impact on the production of renewable energy using an SRG machine. The main steps to achieve the object of this work are: First, the wind turbine was modeled and simulated according to the MPPT. In the second step, the indirect torque control strategy, based on the hysteresis current control for SRG 12/8, was realized. This was undertaken using a proportional integral regulator and the hysteresis controller for the torque in order to obtain the appropriate switching based on an asymmetric bridge converter. Moreover, the SRG has high nonlinear characteristics. Thus, the modeling results of this kind of machine are obtained by the use of the finite element method, with its dynamic study performed by the unique estimation of the electromagnetic torque in its generator mode functioning. Finally, the indirect torque control technique of the SRG has also been associated with the MPPT technique to maximize the efficiency power coefficient. The obtained results approve and validate the efficiency of a proposed MPPT of the wind turbine associated with the SRG. This illustrates, simultaneously, the remarkable effects of the turn-switching angles on the operating performances and the high quality of the produced energy. The importance of the effect of varying the turn-switching angles is also presented and discussed.

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.

Introduction. Power planning and management of practical power systems considering the integration and coordination of various FACTS devices is a vital research area. Recently, several metaheuristic methods have been developed and applied to solve various optimization problems. Among these methods, an artificial ecosystem based optimization has been successfully proposed and applied to solve various industrial and planning problems. The novelty of the work consists in creating an interactive process search between diversification and intensification within the standard artificial ecosystem based optimization. The concept of the introduced variant is based on creating dynamic interaction between production operator and consumer operator during search process. Purpose. This paper introduces an interactive artificial ecosystem based optimization to solve with accuracy the multi objective power management optimization problems. Methods. The solution of the problem was carried out using MATLAB program and the developed package is based on combining the proposed metaheuristic method and the power flow tool based Newton-Raphson algorithm. Results. Obtained results confirmed that the proposed optimizer tool may be suitable to solve individually and simultaneously various objective functions such as the total fuel cost, the power losses and the voltage deviation. Practical value. The efficiency of the proposed variant in terms of solution quality and convergence behavior has been validated on two practical electric test systems: the IEEE-30-bus, and the IEEE-57-bus. A statistical comparative study with critical review is elaborated and intensively compared to various recent metaheuristic techniques confirm the competitive aspect and particularity of the proposed optimizer tool in solving with accuracy the power management considering various objective functions.

An extended Kalman filter and a discrete time adaptive linear quadratic regulator for uninterruptible power supplies are presented. On the control strategy design, the gains are determined by minimizing a cost function which reduces the tracking error and the control signal. An extended Kalman filter identifies and estimates the plant parameters and the unmeasured state variables respectively at different load conditions.