The present work addresses the modelling, control, and simulation of a microgrid integrated wind power system with Doubly Fed Induction Generator (DFIG) using a hybrid energy storage system. In order to improve the quality of the waveforms (voltages and currents) supplied to the grid, instead of a two level-inverter, the rotor of the DFIG is supplied using a three-level inverter. A new adaptive algorithm based on combined Direct Reactive Power Control (DRPC) and fuzzy logic controls techniques is applied to the proposed topology. In this work, two topologies are proposed. In the first one, the active power injected into the grid is smoothened by using an economical hybrid battery and supercapacitor energy storage system. However, in the second one, the excess wind energy is used to produce and store the hydrogen, and then a solid oxide fuel cell system (SOFC) is utilized to regenerate electricity by using the stored hydrogen when there is not enough wind energy. To avoid overcharging, deep discharging of batteries, to mitigate fluctuations due to wind speed variations, and to fulfil the requirement of the load profile, a power management algorithm is implemented. This algorithm ensures smooth output power in the first topology and service continuity in the second. The modelling and simulation results are presented and analysed using Matlab/Simulink.

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.

A novel direct reactive power control strategy based on the three-level inverter topology (DRPC-3N) is proposed for a doubly fed induction generator (DFIG)-based wind power plant system. The robustness against parametric variations and control performances of the presented methodology are analyzed under random wind speeds, taking into account the effect of the heating of the windings as well as the saturation of the magnetic circuit. The performance indices include obtaining a sinusoidal AC-generated current with low THD and less ripples in the output. Moreover, the generator can be considered as a reactive power compensator, which allows for the controlling of the active and reactive power of the stator side connected directly to the grid side using only the rotor converter. In this study, unpredictable conduct of the wind velocity that forces the DFIG to operate through all modes of operation in a continual and successive way is considered. The received wind power is utilized to extract the optimum power by using an appropriate MPPT algorithm, and the pitch angle control is activated during the overspeed to restrict the produced active power. The simulation tests are performed under Matlab/Simulink and the presented results show the robustness and effectiveness of the new DRPC strategy with the proposed topology, which means that the performances are more sophisticated.

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).

This article presents novel artificial intelligence (AI)‐based techniques for controlling wind energy conversion systems, specifically fuzzy logic control and neural networks, known as fuzzy hysteresis‐direct power control (FH‐DPC) and neural hysteresis‐DPC (NH‐DPC), respectively. The primary purpose is to overcome conventional DPC (C‐DPC) limitations in doubly fed induction generator wind turbines (WT‐DFIG), focusing on power quality improvement and enhanced system efficiency. The techniques aim to reduce power ripples and improve the quality of alternating current (AC) grid energy by improving current signal quality in all WTs’ operation modes with WT‐DFIG and all compensation power modes. The suggested techniques are thoroughly examined using the MATLAB/Simulink environment under various wind scenarios, demonstrating a reduction in active power ripples by over 70%, a reduction in reactive power ripples of around 77% on average, a decrease in generated current total harmonic distortions (THDs) by over 70% compared to C‐DPC. The performances of FH‐DPC and NH‐DPC are contrasted with C‐DPC and other previously suggested methods, and it is concluded that the proposed control approaches perform more effectively than them regarding ripples in local reactive compensation and generated active powers as well as THD currents, with FH‐DPC slightly outperforming NH‐DPC. The research indicates that AI can enhance the effectiveness and quality of power generated by wind‐power systems.

This paper presents an innovative analytical model for photovoltaic (PV) systems operating under partial shading conditions (PSCs). The model is developed through a detailed analysis of the current–voltage ( I – V ) curves of PV systems affected by PSCs and employs a straightforward computational algorithm by adjusting every time the number of cells or modules contributing to power generation and calculating the voltage across these cells or modules, the Newton–Raphson algorithm is then used to solve the developed analytical model efficiently, ensuring accurate integration of all data into the equation of the output current. The proposed model accounts for the effects of bypass and blocking diodes, which are critical for managing partial shading and ensuring efficient power flow. It is applicable to various configurations, including individual PV modules and PV arrays in series (S), parallel (P), and series‐parallel (SP) arrangements. The results are validated through comparisons with Simpowersystem tools in the MATLAB‐Simulink environment. The model showcases notably accelerated execution times and dependable convergence toward the global maximum power point (GMPP). Additionally, the proposed algorithm can be implemented using any computational software, highlighting its versatility and potential for practical applications in PV system optimization and real‐time simulation environments.