Archimedes optimization algorithm based parameter extraction of photovoltaic models on a decent basis for novel accurate RMSE calculation

Abstract

Solar photovoltaic (PV) technology stands as a promising alternative to conventional fossil fuel-based power generation, offering pollution-free and low-maintenance energy production. To harness its potential effectively, understanding the power generation process and accurately modeling solar PV systems are essential. Unfortunately, manufacturers often do not provide the necessary parameters for modeling solar cells, making it challenging for researchers. This research employs the Archimedes Optimization Algorithm (AOA), an optimization technique, to determine unknown parameters for the PVM752 GaAs thin film solar cell and the RTC France solar cell. The modeling of these solar cells utilizes both a Single Diode Model (SDM) and a Double Diode Model (DDM). Performance evaluations are conducted using the sum of individual absolute errors (SIAE) and a novel root mean square error (RMSE) method. Comparing the effectiveness of the AOA with other optimization methods, The RMSEs for the AOA applied to the SDM and DDM of RTC France solar cell were 3.7415 × 10–3 and 1.0033 × 10–3. Similarly, for PVM752 GaAs thin film solar cell were 1.6564 × 10–3, and 0.00106365, respectively. The SIAE values for both solar diode models of RTC France cells were 0.071845 and 0.021268, respectively. For the PVM752 GaAs thin film, the corresponding SIAE values were 0.031488 and 0.040224. The results highlight the efficiency of the AOA-based approach, showcasing consistent convergence and a high level of accuracy in obtained solutions. The suggested approach produces superior results with a lower RMSE compared to other algorithms, demonstrating its efficacy in determining solar PV parameters for modeling purposes.