article · IET Renewable Power Generation
Optimising solar photovoltaic systems is essential for maximising energy conversion efficiency, which relies heavily on accurate model parameter extraction. A hybrid algorithm combining the gazelle optimisation algorithm with the Nelder-Mead method, designated as GOANM, addresses this requirement. Initial testing across varied mathematical benchmark functions, including unimodal, multimodal, fixed-dimensional multimodal, and CEC2020 test sets, showed enhanced convergence speed, accuracy, and reliability compared to other optimisation approaches. The algorithm was subsequently applied to extract parameters for single-diode and double-diode models using experimental data from the RTC France solar cell and the Photowatt-PWP201 photovoltaic module. In these evaluations, the hybrid approach achieved superior performance over competing methods, demonstrating rapid convergence, low root mean square values, and close alignment with experimental measurements.
Solar panels require precise mathematical modelling to ensure they operate at peak energy conversion efficiency. By reliably estimating the underlying parameters of solar cells and modules with high speed and low error rates, this computational approach helps engineers and researchers better predict, design, and manage the performance of renewable energy installations.
This method could assist photovoltaic engineers, system designers, and simulation software developers seeking accurate parameter estimation for solar cells and modules. The work represents applied computational research tested against real-world experimental datasets from standard commercial solar products, placing it at an early to intermediate stage of software development prior to integration into commercial photovoltaic design or monitoring tools.
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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.
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DOI: 10.1049/rpg2.12974
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