Wind Turbine (WT)-based Doubly-Fed Induction Generator (DFIG) is a nonlinear system, in which the wind has variable behavior, and the local reactive power depends on the random demand of the AC grid; all these make conventional controls insufficient for an adequate power output. Thus, a new direct power control (DPC) strategy is proposed in this paper, to force the system to track the desired dynamics with higher performance and less drawbacks. This new approach is based on machine learning, Neural-Network- and Neuro-Fuzzy-DPC (NN- and NF-DPC), both are designed to overcome the problems related to power control, wind and local reactive power variations. All operating modes (sub-synchronous, super-synchronous and synchronous modes), with the possibility of local reactive power compensation are considered in this paper. Both NN and NF networks have been trained under the MATLAB interface. The trained NF showed better efficiency than the NN; the learning time is reduced to a few seconds with less computational effort, and less complexity. The effectiveness of both controls has been confirmed through simulation tests using MATLAB software. The obtained results have shown that the NF-DPC strategy presents better performances than NN-DPC and conventional control, with fast response, robustness, less power ripples and reduced Total Harmonic Distortion (THD) in the generated currents.

Due to the instantaneous variation of wind speed, it is suitable to determine the optimal rotational speed that ensures maximum energy efficiency. This article deals with a comparison between different maximum power point tracking algorithms applied to wind energy conversion systems. The most commonly used algorithms are optimal torque control, power signal feedback, tip-speed ratio, and hill-climb search. The conventional hill-climb search method has an important drawback, which is the wrong directionality under rapid wind change. To solve this problem, using a hybrid hill-climb search method is proposed, which consists of combining the optimal torque control and hill-climb search methods. The proposed approach is applied to a wind energy conversion system based on a permanent magnet synchronous generator under a wind speed profile using a MATLAB®-Simulink® (The MathWorks, Natick, Massachusetts, USA) package to achieve the simulation calculations. The obtained results are then presented and compared to those given by classical algorithms to highlight the interest of the proposed approach in terms of efficiency and response speed.

A wind turbine (WT)-based doubly fed induction generator (DFIG) is the most often used generator in the wind conversion system market due to its advantages such as the ability of operating under variable wind speed and its high performance. However, nonlinear dynamical and parameter uncertainties of the DFIG make the controller design of this kind of system a challenging work. Thus, in this study, a novel control strategy was proposed to design the desired system dynamics, to highlight the efficacy of the proposed system, and to improve the performance of the closed-loop system. The proposed controller combines the twelve-sector direct torque control (12-DTC) and the fuzzy controller with modified rules to solve the limitations and shortcomings of the usual methods for the WT-DFIG system. All operation modes, successively and continually, were considered to reflect the true operation of WT-DFIG system subject to random wind speeds. The aims of this work was to ensure an optimal operation of the wind generator, extracting maximum power in the zone II of the WT characteristic, and limiting this power in its maximum value in the case (zone III), to transmit the power generated by the DFIG to the grid-side with minimum losses in the disturbances related to DFIG. Extensive numerical simulations were performed under MATALB/Simulink, where the proposed fuzzy twelve direct torque control (F12-DTC) was compared with conventional nonlinear controls: conventional DTC (C-DTC) and 12-DTC. The simulation results demonstrated clearly that the proposed one had the highest performance and robustness, with a significant reduction in rotor flux and electromagnetic torque ripples and better-generated power quality with low currents’ THD over the conventional strategies (C-DTC and 12-DTC).

Partial shading condition (PSC) is an inevitable issue faced by the photovoltaic (PV) modules, where the $I$ – $V$ curve shows multiple current stairs and the ( $P$ – $V$ ) curve exhibits several peaks. In this article, a collaborative swarm algorithm (CSA) scheme to tackle the PSCs has been proposed. The intelligent mechanism of the CSA method allows the share of the best solution ( $G_{\mathrm {best}}$ ) found between each algorithm dynamically, which diversifies the exploration phase and helps to determine the accurate global maximum power point (GMPP). The proposed method is deterministic as the utilization of random numbers has been avoided, and only two tuning parameters require tuning. A buck–boost converter is used to verify the proposed method experimentally. The results show that the average tracking time is significantly improved by 200% compared with particle swarm optimization (PSO), Jaya, and ant colony optimization based on new pheromone update (ACO-NUP) algorithms and by 85% compared with the ACO-NUP-PSO algorithm. The collaborative approach was found to be superior over the utilization of individual types of metaheuristic algorithms in terms of tracking speed and efficiency.

In this article, a voltage track optimizer based maximum power point tracking controller for photovoltaic systems under challenging partial shading conditions is presented. The proposed method aims to optimize the operating voltage track while searching for the global maximum power peak (GMPP). This is achieved by bounding the search area by dynamic lower and upper voltage limits. In addition, an improved skipping mechanism is integrated to avoid scanning the unnecessary areas that do not contain the GMPP by exploiting the critical observations in the P-V and I-V curves. Furthermore, the initial operating voltage is exploited to accelerate the tracking speed. By doing so, the voltage track length is drastically reduced, which results in reducing the tracking time. The performance of the proposed method is evaluated against two recent GMPPT methods, namely improved team game optimization and modified maximum power trapezium through MATLAB/Simulink environment. Besides, the simulation results are verified experimentally via a buck-boost converter. The obtained results prove the superiority of the proposed algorithm against the compared algorithms in terms of voltage track length, tracking speed, and transient energy losses.