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.

Due to their simplicity, cheapness, and ease of maintenance, induction motors (IMs) are the most widely used motors in the industry. However, if they are not properly controlled, the load torque and motor speed will fluctuate in an unsatisfactory fashion. To effectively control the load torque and speed of these IMs, it is necessary to use specialized drives. The entire system (IMs + Drives) will experience uncertainty, nonlinearities, and disruptions, which calls for an outstanding performance control structure. The sensorless sliding mode predictive torque control (SSM-PTC) for both AC-DC converter and DC-AC inverter, which are utilized for feeding the IM, is investigated in this work. The AC-DC converter is controlled using the SSM-PTC method in order to follow the DC-link reference voltage throughout any changes in the operating point of the IM. While the DC-AC inverter is controlled using a sensorless predictive power control (SPPC). Within a unity power factor, this SPPC regulates the reactive power flow between the motor and the supply to account for the undesirable harmonic components of the grid current. In addition, an experimental performance improvement of SSM-PTC of IM supplied by a 5-leg AC-DC-AC power converter using extended Kalman filter (EKF) without weighting factor (WF) is also studied in this work. Design and implantation of the suggested control systems are performed using a dSPACE 1104 card. The experimental results of the proposed converter control demonstrate that the suggested approach effectively regulated the DC link, reducing load torque and speed fluctuations. In the context of inverter control, a prompt active power response yields a motor current waveform that resembles a sinusoidal pattern, exhibiting minimal levels of harmonic distortion.