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).

In this study, the comparative study of the peristaltic flow of Newtonian and non-Newtonian fluids under the consideration of the magnetic field in the porous inclined channel is investigated. The effects of velocity slip and convective boundary conditions are also considered. Moreover, the variable liquid properties are also taken. The mathematical model is developed with the help of the Jeffrey fluid model in the form of partial differential equations. After that, convert them into dimensional form by using the dimensionless quantities. The resultant system of equations is solved through the perturbation method and presented the solution of velocity, temperature, and concentration in analytical form. The impact of physical parameters on the velocity, temperature, and concentration profiles are highlighted with the help of the graphs. The outcomes revealed that the magnetic parameter slows down the velocity of the fluid while the Darcy number enhanced the velocity and temperature distribution and suppressed the concentration profile.

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