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 advanced strategy combining fuzzy logic and artificial neural networks (ANNs) for direct torque control (DTC) and broken-bar fault diagnosis in induction motors. More specifically, a fuzzy-based controller is used to simultaneously minimize the stator flux and the electromagnetic torque ripples. A neural switching table is then proposed to achieve the interface inverter control. Besides, a closed-loop broken-bar fault detection strategy based on the Hilbert technique (HT) with the discrete wavelet transform (DWT) and ANNs is proposed. The fault detection is performed by analyzing the induction motors stator current by using the combined techniques HT-DWT. The effect of a broken-bar fault on the machine varies according to the number and position of the broken bars. The neural detector was used in order to identify the number of broken bars through only one current measurement. The effectiveness of the developed control has been verified using MATLAB/Simulink and real-time simulation in OPAL-RT 4510. Obtained results show improved performances in terms of torque ripple minimization and stator current quality, evaluated, respectively, at 43.75 and 41.26 as well as a rigorous motor health monitoring.

This paper introduces a robust fault-tolerant control (FTC) for a five-phase permanent magnet synchronous motor (FPPMSM) affected by the third harmonic under an open-circuit fault (OCF). Using field-oriented control, the proposed method demonstrates how to achieve optimal current references for torque decoupling under healthy and faulty conditions. The proposed speed and current loop controllers are based on sliding mode control (SMC), with a nonlinear extended state observer (NESO) that utilizes a hyperbolic tangent function (HTF) to provide feed-forward compensation to the controllers. The results analysis confirmed that the proposed control could enhance the tracking accuracy and robustness to disturbances under various conditions, substantially reducing torque ripples and speed fluctuations under a fault.

A healthy operation of photovoltaic installations (similar to all electrical systems) is always limited by breakdown, degradation due to aging, or imbalance caused by weather conditions. In this context, producing the maximum energy possible with an acceptable form factor is a significant challenge for autonomous systems, especially those connected to the grid. In this paper, we have two main issues to address. The first is determining the maximum power point of an unbalanced photovoltaic field (due to a defect or nonuniform weather conditions affecting the photovoltaic generators). For such a system, the particle swarm optimization (PSO) algorithm remains highly effective because it can easily handle the existence of multiple maxima simultaneously to provide the best possible solution. The second challenge is managing the imbalance between the three phases of the photovoltaic system. In this context, the results of conducted studies propose two approaches to balance and maximize the power supplied by the photovoltaic generator and converters. In addition, the presence of a battery storage system plays dual roles: firstly, compensating the power fluctuations due to nonuniform operating conditions between phases, and secondly, ensuring system power supply during periods of no sunlight exposure. The proposed approaches take into account the constraints imposed on DC voltages and currents to ensure optimal integration with the multilevel inverter stage (cascaded H‐bridge multilevel inverters). This is achieved through selective harmonic elimination control without the need for a filtering system. A comparative study between these two approaches will be conducted to assess their advantages and disadvantages. The battery‐based storage system efficiently absorbs excess energy and provides energy during deficits, thanks to a flexible control mechanism that allows easy switching between different battery groups and phases.