A novel approach to parameter estimation of photovoltaic systems using hybridized optimizer

Due to rise in solar energy applications, research in Photovoltaic (PV) systems is increasingly taking precedence. Ideally, one requires an accurate model of the PV based cell/module so as to theoretically investigate the performances of various control schemes. In this work, a novel hybridized optimizer, hybridized interior search algorithm (HISA), has been explored for effective parameters estimation of PV cell/module. Single and double diode-based models of PV cell/module have been estimated from their respective single I - V nonlinear characteristic using experimental data from five case studies comprising of two cells and three modules using mono crystalline, multi-crystalline and thin film-based PV technology. Additionally, based on used model and PV technology specific insights on new bounds for parameter estimation are investigated. Modeling performance of HISA has been assessed using root mean square error (RMSE), weighted RMSE and mean absolute error, between computed and experimental data. A comparative study with the 34 recent published works, in reported five case studies including analytical, deterministic and metaheuristics-based methods, revealed that HISA based parameter estimation is superior. Therefore, based on the presented detailed investigation, it is concluded that the proposed HISA is a promising optimization technique for PV cell/module estimation.