COMPREHENSIVE ASSESSMENT OF RAMD IN SOLAR PHOTOVOLTAIC SYSTEM

This study aims to conduct a comprehensive performance analysis of solar photovoltaic systems by evaluating key reliability metrics within the framework of Reliability, Availability, Maintainability, and Dependability. Specifically, the study seeks to enhance the system’s maintainability, reliability, dependability, and availability. The system under study comprises four subsystems—panel, controller, battery, and inverter—arranged in a series configuration. The study employs a Markovian birth-death process with differential-difference equations to model the state transitions of the four critical subsystems (panel, controller, battery, and inverter) to capture the dynamic transitions between system states over time, enabling a more realistic representation of system behavior, allows for the derivation of closed-form solutions for various reliability metrics, facilitating analytical evaluations., inclusion of exponential, Lindley, exponentiated Weibull geometric (EWG), exponentiated Lomax (EL), and exponentiated Weibull distributions (EWG) enables more flexible failure and repair rate modeling, addressing the limitations of traditional exponential assumptions, and aligns well with the maintenance strategies of solar photovoltaic systems, where components undergo periodic repair and replacement. By modeling the system using a Markovian birth-death process and incorporating advanced statistical distributions for failure and repair rates, the study aims to optimize system performance and provide insights for designing robust and sustainable solar PV architectures. Numerical experiments are conducted, and the results are presented in tables and graphs. The findings indicate optimal system performance and dependability are achieved when the overall system failure rate is minimized. This study provides valuable insights for performance analysis, guiding the design of robust system architectures and the development of effective maintenance strategies to enhance system performance, durability, production efficiency, and revenue generation.