Recently, solar power generation is significantly contributed to growing renewable sources of electricity all over the world. The reliability and availability improvement of solar photovoltaic (PV) systems has become a critical area of interest for researchers. Reliability, availability, and maintainability (RAM) is an engineering tool used to address operational and safety issues of systems. It aims to identify the weakest areas of a system which will improve the overall system reliability. In this paper, RAM analysis of grid-connected solar-PV system is presented. Elaborate RAM analysis of these systems is presented starting from the sub-assembly level to the subsystem level, then the overall system. Further, an improved Reliability Block Diagram is presented to estimate the RAM performance of seven practical grid-connected solar-PV systems. The required input data are obtained from worldwide databases of failures, and repair of various subassemblies comprising various meteorological conditions. A novel approach is also presented in order to estimate the best probability density function for each sub-assembly. The monitoring of the critical subassemblies of a PV system will increase the possibility not only for improving the availability of the system, but also to optimize the maintenance costs. Additionally, it will inform the operators about the status of the various subsystems of the system.

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Abstract. Magnetohydrodynamic (MHD) flow of blood has been studied under the influence of body acceleration. With the help of Laplace and finite Hankel transforms, an exact solution is obtained for the unsteady flow of blood as an electrical conducting, incompressible and elastico-viscous fluid in the presence of a magnetic field acting along the radius of the pipe. Analytical expressions for axial velocity, fluid acceleration and flow rate has been obtained.

One of the most significant barriers to broadening the use of solar energy is low conversion efficiency, which necessitates the development of novel techniques to enhance solar energy conversion equipment design. The correct modeling and estimation of solar cell parameters are critical for the control, design, and simulation of PV panels to achieve optimal performance. Conventional optimization approaches have several limitations when solving this complicated issue, including a proclivity to become caught in some local optima. In this study, a Growth Optimization (GO) algorithm is developed and simulated from humans’ learning and reflection capacities in social growing activities. It is based on mimicking two stages. First, learning is a procedure through which people mature by absorbing information from others. Second, reflection is examining one’s weaknesses and altering one’s learning techniques to aid in one’s improvement. It is developed for estimating PV parameters for two different solar PV modules, RTC France and Kyocera KC200GT PV modules, based on manufacturing technology and solar cell modeling. Three present-day techniques are contrasted to GO’s performance which is the energy valley optimizer (EVO), Five Phases Algorithm (FPA), and Hazelnut tree search (HTS) algorithm. The simulation results enhance the electrical properties of PV systems due to the implemented GO technique. Additionally, the developed GO technique can determine unexplained PV parameters by considering diverse operating settings of varying temperatures and irradiances. For the RTC France PV module, GO achieves improvements of 19.51%, 1.6%, and 0.74% compared to the EVO, FPA, and HTS considering the PVSD and 51.92%, 4.06%, and 8.33% considering the PVDD, respectively. For the Kyocera KC200GT PV module, the proposed GO achieves improvements of 94.71%, 12.36%, and 58.02% considering the PVSD and 96.97%, 5.66%, and 61.20% considering the PVDD, respectively.