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

Abstract

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.

Performance evaluation of logarithmic spiral search and selective mechanism based arithmetic optimizer for parameter extraction of different photovoltaic cell models

“…In the literature, various approaches including analytical, numerical, and metaheuristic methods have been employed for accurate parameter estimation of PV cells [ 47 ]. While analytical methods offer simplicity and speed, they often rely on numerous mathematical equations, some of which are derived based on simplifying assumptions, potentially limiting their accuracy [ 48 ].…”

Parameters optimization of solar PV cell using genetic-iterative algorithm

“…Lastly, the study outlined in [31] explores the efficacy of Newton-Raphson and modified versions for obtaining solar cell parameters, highlighting the efficiency of the proposed method in solving the model with fewer iterations. Additionally, [32] introduces a novel hybrid algorithm showcasing superior convergence speed, accuracy, and reliability compared to other optimization methods, thereby enhancing performance and efficiency in renewable energy systems.…”