To ensure the full benefits from wind generation systems, an effective maximum power point tracking (MPPT) technique should be implemented. In this article, an adaptive fuzzy logic controller (AFLC) is presented as a control methodology for producing the peak power from a permanent magnet synchronous generator (PMSG)-based wind turbine (WT). Regardless of the importance and significance of modeling and simulation processes, experimental studies occupy the most important aspect and remain a big challenge. The first part of this article is devoted to the simulation and modeling of PMSG along with the control system by MATLAB/Simulink ® package to develop the control system. To justify the simulation results, experimental validation tests are presented under the same status of wind speed profile and run period. The experimental validation is conducted using the DSPACE DS1104 control board and is compared with simulation results. Moreover, for a realistic response, actual wind speed data is utilized in this research depending on measured data from Ras Ghareb wind farm in the Gulf of Suez, Egypt. The obtained results confirm the superiority of AFLC compared with fuzzy logic controller and conventional PI control schemes. In addition, good tracking with high accuracy is obtained regarding experimental and simulations results. Moreover, an evaluation indices are employed for WT performance based on gross system efficiency and integral of the absolute error (IAE). These indices are used to demonstrate the feasibility of the AFLC methodology compared with traditional approaches under the same wind turbine status.

Wind generators have attracted a lot of attention in the realm of renewable energy systems, but they are vulnerable to harsh environmental conditions and grid faults. The influence of the manta ray foraging optimizer (MRFO) on the dynamic performance of the two commonly used variable speed wind generators (VSWGs), called the permanent magnet synchronous generator (PMSG) and doubly-fed induction generator (DFIG), is investigated in this research article. The PMSG and DFIG were exposed to identical wind speed changes depending on their wind turbine characteristics, as well as a dangerous three-phase fault, to evaluate the durability of MRFO-based wind side controllers. To protect VSWGs from hazardous gusts and obtain the optimum power from incoming wind speeds, we utilized a pitch angle controller and optimal torque controller, respectively, in our study. During faults, the commonly utilized industrial approach (crowbar system) was exclusively employed to aid the studied VSWGs in achieving fault ride-through (FRT) capability and control of the DC link voltage. Furthermore, an MRFO-based PI controller was used to develop a crowbar system. The modeling of PMSG, DFIG, and MRFO was performed using the MATLAB/Simulink toolbox. We compared performances of PMSG and DFIG in reference tracking and resilience against changes in system parameters under regular and irregular circumstances. The effectiveness and reliability of the optimized controllers in mitigating the adverse impacts of faults and wind gusts were demonstrated by the simulation results. Without considering the exterior circuit of VSWGs or modifying the original architecture, MRFO-PI controllers in the presence of a crowbar system may help cost-effectively alleviate FRT concerns for both studied VSWGs.

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Modern wind power systems have recently tended to focus on achieving fast-tracking wind speeds (WSs), high maximum power point tracking (MPPT) efficacy without mechanical sensors, and high performance under uncertain WS together with an effective control system. Therefore, a sensorless MPPT method is introduced, which calculates the actual WS to save system installation costs and boost performance levels. The implemented MPPT method is based on the approximating of the 3-order polynomial to the aerodynamics torque power coefficient. In this study, three-speed control strategies (SCSs) for a gridconnected permanent magnet synchronous wind generator (PMSWG) are examined and assessed. Harris Hawks' algorithm (HHA)-based PI controller (HHA-PIC) is used in place of (the conventional proportionalintegral controller (CPIC), and adaptive fuzzy logic controller (AFLC)) as a speed controller to overcome their drawbacks. To track the generator speed to the desired speed, the HHA-PIC is used. All the CPIC, AFLC, and HHA-PIC have been carefully thought out and constructed to satisfy the speed control loop's responsive performance. Additionally, a comparison of SCSs amongst the categories under investigation is done. The effect of HHA on the functionality of SCS is verified using MATLAB/SIMULINK. To ensure the efficacy and supremacy of the HHA-PIC over the CPIC and AFLC, a wide variety of WSs (step change, ramp, and real fluctuations) are applied. Finally, it can be said that HHA is a crucial remedy for the problems with CPIC and is superior to AFLC.INDEX TERMS Adaptive FLC, efficiency, Harris Hawks' algorithm (HHA), MPPT, PMSG, real wind variations, wind speed estimation.

This paper studies the effects of increased temperature on the performance of the three phase induction motor. An electrical, mechanical, and thermal model of an induction motor is used to study the change of the motor parameters. The proposed model is developed using Matlab-Simulink. The experimental work is implemented in order to investigate the effect of temperature rise on the induction machine. Simulation and experimental results were obtained to monitor the effects of increased temperature on the stator resistance, stator current, rotor speed, stator flux and motor torque at different loads.