Comparative analysis of the hybrid gazelle-Nelder-Mead algorithm for parameter extraction and optimization of solar photovoltaic systems

The pressing need for sustainable energy solutions has driven significant research in optimizing solar photovoltaic (PV) systems which is crucial for maximizing energy conversion efficiency. Here, a novel hybrid gazelle-Nelder-Mead (GOANM) algorithm is proposed and evaluated. The GOANM algorithm synergistically integrates the gazelle optimization algorithm (GOA) with the Nelder-Mead (NM) algorithm, offering an efficient and powerful approach for parameter extraction in solar PV models. This investigation involves a thorough assessment of the algorithm's performance across diverse benchmark functions, including unimodal, multimodal, fixed-dimensional multimodal, and CEC2020 benchmark functions. Notably, the GOANM consistently outperforms other optimization approaches, demonstrating enhanced convergence speed, accuracy, and reliability. Furthermore, the application of the GOANM is extended to the parameter extraction of the single diode and double diode models of RTC France solar cell and PV model of Photowatt-PWP201 PV module. The experimental results consistently demonstrate that the GOANM outperforms other optimization approaches in terms of accurate parameter estimation, low root mean square values, fast convergence, and alignment with experimental data. These results emphasize its role in achieving superior performance and efficiency in renewable energy systems. This study compares a new hybrid Gazelle-Nelder-Mead (GOANM) algorithm for parameter extraction in solar cells, evaluating single diode (SDM) and double diode (DDM) models. GOANM's performance is first tested on CEC2020 and classical benchmark functions. Then, it optimizes SDM and DDM for the RTC France solar cell and Photowatt-PWP201 PV module. Results demonstrate the algorithm's superior performance. image