The stagnation point flow of unsteady compressible Casson hybrid nanofluid flow over a vertical stretching sheet was analyzed. The comparative study of Yamada Ota, Tiwari Das, and Xue hybrid nanofluid models was performed. The Lorentz force was applied normal to flow directions. The effect of nonlinear radiation was studied. We considered the SWCNT (signal wall carbon nanotube) and MWCNT (multi-wall carbon nanotube) with base liquid (water). Under the flow suppositions, a mathematical model was settled by means of boundary layer approximations in terms of partial differential equations. The suitable transformation was developed by using the lie symmetry method. Partial differential equations were transformed into ordinary differential equations by suitable transformations. The dimensionless system was elucidated through a numerical technique named bvp4c. The impacts of pertinent flow parameters on skin friction, Nusselt number, and temperature and velocity distributions were depicted through tabular form as well as graphical form. In this study, the Yamada Ota model achieved a higher heat transfer rate compared to the Tiwari Das and Xue hybrid nanofluid models. The skin friction (${\mathrm{C}}_{\mathrm{fx}}{\mathrm{Re}}^{-1/2}$) increased and temperature gradient (${\mathrm{Nu}}_{\mathrm{x}}{\mathrm{Re}}^{-1/2}$) declined due to the increment of solid nanoparticle concentration (${\mathsf{\varphi }}_2$). Physically, skin friction increased because the higher values of the solid nanoparticles increased resistance to the fluid motion.

Modern photovoltaic (PV) systems have received significant attention regarding fault detection and diagnosis (FDD) for enhancing their operation by boosting their dependability, availability, and necessary safety. As a result, the problem of FDD in grid-connected PV (GCPV) systems is discussed in this work. Tools for feature extraction and selection and fault classification are applied in the developed FDD approach to monitor the GCPV system under various operating conditions. This is addressed such that the genetic algorithm (GA) technique is used for selecting the best features and the artificial neural network (ANN) classifier is applied for fault diagnosis. Only the most important features are selected to be supplied to the ANN classifier. The classification performance is determined via different metrics for various GA-based ANN classifiers using data extracted from the healthy and faulty data of the GCPV system. A thorough analysis of 16 faults applied on the module is performed. In general terms, the faults observed in the system are classified under three categories: simple, multiple, and mixed. The obtained results confirm the feasibility and effectiveness with a low computation time of the proposed approach for fault diagnosis.