This paperpresents a direct power control (DPC) method using an adaptive reference model, known as Model Reference Adaptive Control (MRAC), for the Doubly Fed Induction Generator (DFIG). This approach addresses to get over the drawbacks of the classic DPC, which relies solely on PID controllers. These limitations are often addressed with the compromise speed/efficiency trade-off and divergence from peak power in the event of rapid variation in wind speed. The Doubly Fed Induction Generator (DFIG) mathematical equations in the d-q reference frame are provided. Subsequently, a direct power control (DPC) algorithm is developed for controlling of the DFIG, employing PID controllers, along with space vector modulation (SVM) to maintain a steady switching frequency. The Maximum Power Point Tracking (MPPT) technique keeps the stator side power factor at unity level. The conventional PID controllers are replaced with Model Reference Adaptive Control (MRAC) in the DPC framework. Furthermore, the efficacy of DPC-based MRAC is assessed and contrasted with that of the PID controller. Results from robustness tests conducted in the MATLAB/Simulink environment demonstrate that MRAC exhibits efficiency, superior dynamic performance, and enhanced robustness against parameter variations.

The use of MPPT control is essential for the optimization of a photovoltaic system. It consists in controlling the static converter to reach the maximum power of the photovoltaic generator. Indeed, the MPP research is based on the variation of the duty cycle according to the evolution of the input parameters. the current and the voltage and consequently the power of the photovoltaic generator until the MPP is reached. The MPPT algorithm can be more or less complicated to find the MPP. In our paper we have presented three MPPT commands: perturb and observe, fuzzy logic, particle swarm optimization MATLAB/SIMIULINK is used. The results of the simulation illustrate the high tracking performance of the proposed technique under different climatic conditions.

The performance of a photovoltaic system is strongly affected by the environmental conditions which it is subjected such as random atmospheric variations. In order to improve the performance of a photovoltaic system, the work of this paper is devoted to the comparative study between the following MPPT algorithms: the perturbation and observation algorithm (P&O) and the particle swarm optimization algorithm PSO. These two algorithms are tested under various atmospheric conditions and evaluated in terms of efficiency, stability, speed, and robustness. The obtained simulation results show the effectiveness of the PSO than the P&O algorithm.

This paper presents a control design for the irrigation station by sprinkling. The proposed method is applied in order to solve the problem of managing water sources and distributions systems. This paper presents the synthesis of a Fuzzy Logic control applied to the station of irrigation by sprinkling, this method has the advantage of stability conditions of the proposed controller. After presentation of mathematical model of our station, simulation results illustrate the performance of the control strategy.