The impact of Partial Shading Conditions (PSCs) significantly influences the output of Photovoltaic Systems (PVSs). Under PSCs, the Power-Voltage (P-V) characteristic of the PVS unveils numerous power peaks, inclusive of local maxima and a global maximum. The latter represents the optimum power point. Traditional Maximum Power Point Tracking (MPPT) algorithms struggle to track the Global Maximum Power Point (GMPP). To address this, our study emphasizes the creation of a novel algorithm capable of identifying the GMPP. This approach combines the Cuckoo Search (CS) MPPT algorithm with an Integral Super-Twisting Sliding Mode Controller (STSMC) using their benefits to enhance the PVS performance under PSCs in terms of high efficiency, low power losses, and high-speed convergence towards the GMPP. The STSMC is a second-order Sliding Mode Control strategy that employs a continuous control action that attenuates the “chattering” phenomenon, caused when the first-order SMC technique is employed. Indeed, the proposed CS-STSMC-MPPT algorithm consists of two parts. The first one is based on the CS algorithm used for scanning the power-voltage curve to identify the GMPP, and subsequently generating the associated optimal voltage reference. The second part aims to track the voltage reference by manipulating the duty cycle of the boost converter. The proposed CS-STSMC-MPPT algorithm is featured by its strength against uncertainties and modeling errors. The obtained simulation results underline a high convergence speed and an excellent precision of the proposed method in identifying and tracking the GMPP with high efficiency under varying shading scenarios. For comparative purposes, this method is set against the hybrid CS-Proportional Integral Derivative, the conventional CS, the Particle Swarm Optimization, and the Perturb and Observe algorithms under different PSCs, including zero, weak, and severe shading. Simulation conducted in the Matlab/Simulink environment confirms the superior performance of the proposed CS-STSMC-MPPT algorithm in terms of precision, convergence speed, efficiency, and resilience.

A robust direct torque control (DTC) strategy for an induction motor is proposed in this study. In fact, the proposed control strategy is defined by a combination of DTC, space vector modulation (SVM), input–output feedback linearisation (IOFL), a second‐order super‐twisting speed controller (STSC), and sliding‐mode‐load torque and stator‐flux observers with stator resistance estimation. First, non‐linear IOFL is suggested to achieve decoupled flux and torque control, and the SVM technique is utilised to control the inverter switching frequency which decreases the torque ripples and noise. Second, to improve the speed regulation, an STSC is added to an SVM‐DTC‐IOFL scheme. Furthermore, the sliding mode observers of the stator flux and of the load torque are proposed in order to improve the control performances by reducing uncertainties and to prevent the effects of the stator resistance variations. Indeed, this study presents the importance of implementing the suggested SVM‐DTC‐IOFL using a field‐programmable gate array (FPGA) circuit. The main interest of the FPGA implementation is the decrease in the control loop delay, due to the parallel processing offered by the FPGA. The performances of the proposed control algorithm are investigated by digital simulation using a Xilinx system generator tool and experimental implementation utilising FPGA‐Virtex‐5‐ML507.

This paper aims to improve the induction motor performances using a modified Direct Torque Control (DTC). The DTC is featured by its simple structure and fast dynamic response. Also, it does not require a speed sensor and it is less dependent on the motor parameters. However, the basic handicaps of this method are the torque ripples and the current distortions due to the presence of the hysteresis controllers. This paper aims; firstly, to replace the hysteresis controllers and the switching table by a fuzzy logic system in order to overcome these handicaps. Furthermore, the sampling frequency is another parameter which must be investigated to reduce the commutation losses and the harmonic stator current waves. The second purpose of this paper is to reduce the execution time using a Field Programmable Gate Array (FPGA). The FPGA is able to execute the Fuzzy DTC (FDTC) with a higher sampling frequency, thanks to its parallel processing relative to the Digital Signal Processor (DSP). The FDTC model is verified by simulation using system generator tool and validated by experimentation utilizing an FPGA Virtex 5. Thus, excellent performances at low processing time are obtained using the Xilinx FPGA Virtex 5.

This paper presents a hardware implementation upon a Field Programmable Gate Array (FPGA) of improved Vector Control Strategies (VCSs) based on a Fuzzy Logic System (FLS) of a grid-connected wind energy conversion system. Usually, the classical VCS is based on fixed-gain Proportional Integral Derivative (PID) controllers, which are known to exhibit limited performance against the nonlinear behavior of wind systems, such as rapid fluctuations of the wind speed and uncertainties of the system parameters. In order to overcome this limitation, an improved VCS based on Fuzzy Gain Scheduling PID controllers (VCS-FGS-PID) is suggested in this work to guarantee good tracking, high accuracy and good robustness under system parameter variations. Indeed, the PID controller gains are tuned, in real-time, by the FLS. In addition, the proposed VCS-FGS-PID methods are implemented on the FPGA in order to reduce the delays and the period of the system of the control loop, thanks to its parallel processing. In fact, the performance of the suggested VCS-FGS-PID approaches are proved by digital simulation with the Xilinx system generator tool under Matlab/Simulink, in addition to an experimental hardware-in-the-loop implementation using the FPGA. The obtained results demonstrate that the proposed VCS-FGS-PID techniques offer better performance as regards good tracking and high accuracy and good robustness against stator resistance variability compared to VCS-PI.