Opposition decided gradient-based optimizer with balance analysis and diversity maintenance for parameter identification of solar photovoltaic models

The solar photovoltaic (PV) parameter estimation/identification is a complicated optimization process that directly affects the performance of PV systems if the internal parameters of PV cells are not estimated accurately. Finding the precise and accurate parameters of PV models is the primary gateway to the PV system design to mimic their actual behavior. Numerous optimization algorithms are used to find the cell/module parameters, however, most of these algorithms suffer from the high computational burden, local optima trap, and frequent parameter tuning to get the best results. A metaheuristic algorithm called gradient-based optimization algorithm (GOA) is recently introduced to solve numerical optimization and engineering design problems. Nevertheless, the GOA appears to be trapped in sub-optimal locations, increasing computational time to get the best results. Thus, this paper recommends an enhanced GOA by employing an opposition-based learning mechanism to generate more precise solutions. Therefore, this paper proposes an enhanced variant, called opposition-based GOA (OBGOA), to identify the electrical parameters of various PV models, such as the single-diode model (SDM) and double-diode model (DDM). Numerous experimental data profiles are considered to classify the parameters of the SDM and DDM. The obtained results show that the OBGOA can estimate accurate and precise parameters than the other algorithms. In addition, statistical data analysis of various algorithms is presented for all the PV models. The results demonstrated that the proposed OBGOA could find circuit parameters of the cell and the modules accurately and effectively. This study is backed up by additional online guidance and support at https://premkumarmanoharan.wixsite.com/mysite.