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Wind energy is an environmentally friendly renewable resource, widely regarded as one of the most effective means to achieve sustainable development goals. A large number of variable-speed wind turbines (WT) around the world utilize double fed induction generators (DFIG). In the typical configuration of these systems, the stator is connected directly to the grid, while the wound rotor is powered via a bidirectional converter. This DFIG-WT configuration is effective for converting wind power due to several advantages: it operates at various speeds while keeping the stator frequency synchronized with the grid, supports both sub-synchronous and super-synchronous operating modes, and features a power converter that is minimized on the rotor side. Controlling such system presents a challenging engineering problem due to their nonlinear and interconnected dynamic models. The DFIG-WT must operate under severe operating conditions such as stochastic wind variations and parametric changes. Numerous robust controllers have been developed for DFIG-WT, focusing on achieving asymptotic stability in closed-loop control systems to ensure tracking error convergence over infinite time. Ensuring finite-time error convergence is essential in practice. This paper introduces a new adaptive neural network finite-time control approach for a variable-speed DFIG-WT. The main control objective is power extraction maximization while enhancing the wind energy system performance regarding convergence rate, tracking precision, and robustness to uncertainties. The proposed control approach utilizes adaptive neural network systems to handle system uncertainties effectively. The closed-loop control system finite-time stability is thoroughly confirmed and established through rigorous verification using the concept of finite-time Lyapunov stability of nonlinear systems. The effectiveness of the suggested controller is validated in numerical simulation using the Matlab/Simulink software. [ABSTRACT FROM AUTHOR]

This work focuses on the issue of designing a fixed-time fuzzy adaptive controller for variable-speed doubly fed induction generator-based wind turbine. The main goal is to optimize the wind turbine power production, while improving the performance of the overall control system such as, convergence speed, tracking accuracy, and robustness. For the control design, the backstepping technique is adopted, and fuzzy adaptive systems are employed as universal approximators to estimate continuous functional uncertainties. Using fixed–time stability concept, a detailed mathematical stability proof of the closed-loop control system is performed. Simulation results are presented to testify the efficiency of the designed control approach. [ABSTRACT FROM AUTHOR]

Summary: This article investigates the devise issue of a practical finite‐time output‐tracking control for uncertain nonlinear nonstrict‐feedback systems subject simultaneously to time‐varying output constraints (TVOC), actuator saturation, and bounded unmatched disturbances. An adaptive fuzzy approximator‐based control system is devised using the dynamic surface control (DSC) concept. System output constraints are addressed by employing an appropriate barrier Lyapunov function. Adaptive fuzzy disturbance observers are constructed to practically deal with time‐varying unmatched but bounded disturbances. It is verified via Lyapunov stability analysis and simulation experiments that all closed‐loop signals remain bounded, the output tracking error practically converges in finite time to a tiny but adjustable neighborhood around the origin, and the system output stays within its required time‐varying bounds. [ABSTRACT FROM AUTHOR]

The experimental liquid-liquid equilibrium (LLE) data for the extraction of methanol from aqueous solution using mixed solvents (toluene + cyclohexanol) at 291.15 K and atmospheric pressure at the ratios (0, 0.25, 0.4, 0.6, 0.8, 1) was studied. The values of the solvent ratio 0 and 1 led us to study the liquid-liquid equilibrium of the ternary systems (water/methanol/cyclohexanol) and (water/methanol/toluene). The compositions of the coexisting phases were determined by the cloud point method. The tie line data for ternary and quaternary systems were correlated by NRTL model. The Othmer-Tobias and Hand correlations were also tested, to verify the reliability of the experimental tie lines data. The separation factor and the distribution coefficient were calculated from the experimental data to evaluate the effect of using mixed solvents. It was found that the ratio M = 0.4 is the best suited for the liquid-liquid extraction of methanol from water. [ABSTRACT FROM AUTHOR]