Dynamic response and low voltage ride-through enhancement of brushless double-fed induction generator using Salp swarm optimization algorithm.

A brushless double-fed induction generator (BDFIG) has shown tremendous success in wind turbines due to its robust brushless design, smooth operation, and variable speed characteristics. However, the research regarding controlling of machine during low voltage ride through (LVRT) need greater attention as it may cause total disconnection of machine. In addition, the BDFIG based wind turbines must be capable of providing controlled amount of reactive power to the grid as per modern grid code requirements. Also, a suitable dynamic response of machine during both normal and fault conditions needs to be ensured. This paper, as such, attempts to provide reactive power to the grid by analytically calculating the decaying flux and developing a rotor side converter control scheme accordingly. Furthermore, the dynamic response and LVRT capability of the BDFIG is enhanced by using one of the very intelligent optimization algorithms called the Salp Swarm Algorithm (SSA). To prove the efficacy of the proposed control scheme, its performance is compared with that of the particle swan optimization (PSO) based controller in terms of limiting the fault current, regulating active and reactive power, and maintaining the stable operation of the power system under identical operating conditions. The simulation results show that the proposed control scheme significantly improves the dynamic response and LVRT capability of the developed BDFIG based wind energy conversion system; thus proves its essence and efficacy.

Thermal decomposition of propylene oxide with different activation energy and Reynolds number in a multicomponent tubular reactor containing a cooling jacket.

In this article, we are focusing on heat and mass transfer through a Multicomponent tubular reactor containing a cooling jacket by thermal decomposition of propylene oxide in water. The chemical reaction is an irreversible, 1st order reaction and an exothermic reaction that yields propylene glycol with enthalpy = -84,666 J/mol. The constant rate of the reaction is followed by the Arrhenius equation in which the activation energy is taken on a trial basis in the range from 75,000 to 80,000 J/mol with a fixed frequency factor. For the fluid to flow, the Reynolds number is kept in the range from 100 to 1000. The three partial differential equations of mass, momentum, and energy are coupled to study heat and mass transfer in a tubular reactor by using the chemistry interface in COMSOL Multiphysics 5.4. The initial concentration of propylene oxide is tested in the range from 2 to 3% and the thermal conductivity of the mixture is tested in the range 0.599-0.799. It was found that the amount deactivated of the compound decreases with an increase in Reynolds number. Propylene oxide is decomposed at about 99.8% at Re = 100 at lower activation energy and gives the total maximum enthalpy change in the tubular reactor. Observing the relationship between Sherwood numbers to Nusselt numbers, it was deducted that the convective heat transfer is opposite to convective mass transfer for high Reynolds numbers.

Computational Analysis of Nanoparticle Shapes on Hybrid Nanofluid Flow Due to Flat Horizontal Plate via Solar Collector.

The present work discusses the 2D unsteady flow of second grade hybrid nanofluid in terms of heat transfer and MHD effects over a stretchable moving flat horizontal porous plate. The entropy of system is taken into account. The magnetic field and the Joule heating effects are also considered. Tiny-sized nanoparticles of silicon carbide and titanium oxide dispersed in a base fluid, kerosene oil. Furthermore, the shape factors of tiny-sized particles (sphere, bricks, tetrahedron, and platelets) are explored and discussed in detail. The mathematical representation in expressions of PDEs is built by considering the heat transfer mechanism owing to the effects of Joule heating and viscous dissipation. The present set of PDEs (partial differential equations) are converted into ODEs (ordinary differential equations) by introducing suitable transformations, which are then resolved with the bvp4c (shooting) scheme in MATLAB. Graphical expressions and numerical data are obtained to scrutinize the variations of momentum and temperature fields versus different physical constraints.

RK4 and HAM Solutions of Eyring-Powell Fluid Coating Material with Temperature-Dependent-Viscosity Impact of Porous Matrix on Wire Coating Filled in Coating Die: Cylindrical Co-ordinates.

In this work, we studied the impacts of transmitting light, nonlinear thermal, and micropolar fluid mechanics on a wire surface coating utilizing non-Newtonian viscoelastic flow. Models with temperature-dependent variable viscosity were used. The boundary layer equations governing the flow and heat transport processes were solved using the Runge-Kutta fourth order method. A distinguished constituent of this study was the use of a porous matrix that acted as an insulator to reduce heat loss. In this paper we discuss the effects of numerous development parameters, including ß0, Q, m, Ω, Kp, and Br (non-Newtonian parameter, heat-producing parameter, viscosity parameter, variable viscosity parameter, porosity parameter, and Brinkman number, respectively). Furthermore, the effects of two other parameters, D and M, are also discussed as they relate to velocity and temperature distributions. We observed that the velocity profiles decreased with increasing values of Kp. Fluid velocity increased as the values of M, Br, N, and D increased, while it decreased when the values of Kp, Q and D increased. For increasing values of M, the temperature profile showed increasing behavior, while Br and Q showed decreasing behavior. Furthermore, the present work is validated by comparison with HAM and previously published work, with good results.

Prediction of thermal conductivity of polyvinylpyrrolidone (PVP) electrospun nanocomposite fibers using artificial neural network and prey-predator algorithm.

In this study, multilayer perception neural network (MLPNN) was employed to predict thermal conductivity of PVP electrospun nanocomposite fibers with multiwalled carbon nanotubes (MWCNTs) and Nickel Zinc ferrites [(Ni0.6Zn0.4) Fe2O4]. This is the second attempt on the application of MLPNN with prey predator algorithm for the prediction of thermal conductivity of PVP electrospun nanocomposite fibers. The prey predator algorithm was used to train the neural networks to find the best models. The best models have the minimal of sum squared error between the experimental testing data and the corresponding models results. The minimal error was found to be 0.0028 for MWCNTs model and 0.00199 for Ni-Zn ferrites model. The predicted artificial neural networks (ANNs) responses were analyzed statistically using z-test, correlation coefficient, and the error functions for both inclusions. The predicted ANN responses for PVP electrospun nanocomposite fibers were compared with the experimental data and were found in good agreement.