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In this study, a new control approach is introduced for active/reactive power control in modernized microgrids (MMGs). The dynamics of MMG are considered to be unknown and a fuzzy reference tracking linear quadratic regulator (FRT-LQR) is designed. To tackle the effect of uncertainties and faults such as short-Circuit, weak connection, unbalanced grids, an optimal H ∞ -based deep learned control (OHDLC) is presented. The main contributions are: (1) The dynamics are unknown, and are online identified by the restricted Boltzmann machines (RBMs). (2) The parameters in hidden layers are tuned by the unsupervised contrastive divergence (UCD) algorithm, and the parameters in the output layers are tuned by the designed Lyapunov based learning rules that ensure the stability. (3) The designed H ∞ -based supervisor compensates the perturbations. (4) Several simulations, comparisons, and real-time examination as Hardware-in-the Loop (HiL) setup verify the applicability of the suggested control method. A comparison between the suggested approach and related controllers shows that the designed controller is more robust and accurate. In the suggested method, besides the fact that the deep learning approach improves the accuracy, the designed H ∞ -based supervisor also enhances the robustness. [ABSTRACT FROM AUTHOR]

In this study, the efficacy of adding nanoparticles of MWCNTs to the base fluid of paraffin on the thermal conductivity has been investigated. MWCNTs-paraffin samples were prepared at different mass fractions using two-step method. Due to the dependency of thermal conductivity on temperature, experiments were performed at temperatures of 25, 40, 55 and 70 °C. It was found that at any temperature and nanotubes concentration, the incorporation of nanotubes into the base fluid improves the thermal conductivity. Based on the results, at the temperature of 70 °C, the incorporation of nanotubes with a mass fraction of 5% causes up to 40.6% improvement in thermal conductivity. It can be concluded that at higher temperatures, the addition of nanotubes has more positive effects on the thermal conductivity. Also, the higher the mass fraction of nanotubes, the greater the sensitivity of thermal conductivity to temperature. [ABSTRACT FROM AUTHOR]

In this research, the average Nusselt number in tall and narrow enclosures in the presence of mixed convection of a water-based nanofluid (H2O–Cu–TiO2) is determined and compared. The effect of quantities such as Richardson number of 0.01–100, the volume fractions of 0–2% and geometric shape of the enclosure on average Nusselt number and maximum value of flow function is numerically investigated. The FVM and SIMPLER algorithm are used for resolving the nonlinear equations. According to numerical results, for all the Richardson number and two tall and narrow enclosures, the average Nusselt number rises with growing the nanoparticles volume fractions. Comparison of the tall and narrow enclosures shows that for obtaining the highest heat transfer, by growing the nanoparticles volume fractions under similar conditions, it is better to use tall enclosures for large Richardson number and narrow enclosures for small Richardson number. The highest enhancement of the average Nusselt number with increasing the nanoparticles volume fractions for narrow enclosures was 10.44% at the Ri = 0.01. Whereas in the tall enclosures, the highest increase in the average Nusselt number was 14.51% at Ri = 100. For all the Richardson number and two tall and narrow enclosures, the maximum flow function value of the nanoparticles increases with increasing volume fractions. This increase in small Richardson number is greater than in large Richardson number. [ABSTRACT FROM AUTHOR]