Parameter extraction of photovoltaic models using a comprehensive learning Rao-1 algorithm. (15th January 2022) - Farah, Anouar; Belazi, Akram; Benabdallah, Feres; Almalaq, Abdulaziz; Chtourou, Mohamed; Abido, M.A.
Highlights: The comprehensive learning Rao-1 (CLRao-1) is proposed for parameter estimation of PV cells and modules. Three mutually exclusive equations using quantum and Levy flight strategy are suggested to improve the exploration and exploitation abilities of the standard Rao-1. The efficacy of the proposed CLRao-1 is demonstrated using a comparison with several well-established meta-heuristic algorithms. The validity of the obtained model is achieved by comparing experimental and simulated data. Abstract: The environmental impact and scarcity of fossil fuels have led to a tremendous increase in the demand and use of renewable energy resources. The use of photovoltaic (PV) energy is due to several benefits, such as low maintenance and operating costs. The modeling of the PV systems process requires the identification of PV cells parameters, which can be formulated in an optimization problem. This problem is a very challenging task as it is nonlinear and multimodal. Therefore, metaheuristic algorithms suggested recently may present unsatisfactory results at the level of solution quality and convergence speed. To ensure the effectiveness of any algorithm, the equilibrium between exploration and exploitation must be guaranteed. To achieve an accurate model of the photovoltaic cells, a comprehensive learning Rao-1 (CLRao-1) optimization algorithm is proposed in this work. Rao-1 is chosen due to its simplicity, easy comprehension, and implementation. The suggested modifications based on three mutually exclusive search equations do not affect its easiness. The effectiveness of our algorithms is assessed through PV cells and three modules. The results of the experimental and simulated data were in high agreement. Moreover, our algorithm achieved the best results in terms of reliability and accuracy.