Shuffled Puma Optimizer for Parameter Extraction and Sensitivity Analysis in Photovoltaic Models

En-Jui Liu (), Rou-Wen Chen, Qing-An Wang and Wan-Ling Lu
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En-Jui Liu: Department of Green Energy and Information Technology, National Taitung University, Taitung 950309, Taiwan
Rou-Wen Chen: Department of Green Energy and Information Technology, National Taitung University, Taitung 950309, Taiwan
Qing-An Wang: Department of Green Energy and Information Technology, National Taitung University, Taitung 950309, Taiwan
Wan-Ling Lu: Department of Green Energy and Information Technology, National Taitung University, Taitung 950309, Taiwan

Energies, 2025, vol. 18, issue 15, 1-25

Abstract: Photovoltaic (PV) systems are the core technology for implementing net-zero carbon emissions by 2050. The performance of PV systems is strongly influenced by environmental factors, including irradiance, temperature, and shading, which makes it difficult to characterize the nonlinear and multi-coupling behavior of the systems. Accurate modeling is essential for reliable performance prediction and lifespan estimation. To address this challenge, a novel metaheuristic algorithm called shuffled puma optimizer (SPO) is deployed to perform parameter extraction and optimal configuration identification across four PV models. The robustness and stability of SPO are comprehensively evaluated through comparisons with advanced algorithms based on best fitness, mean fitness, and standard deviation. The root mean square error (RMSE) obtained by SPO for parameter extraction are 8.8180 × 10 −4 , 8.5513 × 10 −4 , 8.4900 × 10 −4 , and 2.3941 × 10 −3 for the single diode model (SDM), double diode model (DDM), triple diode model (TDM), and photovoltaic module model (PMM), respectively. A one-factor-at-a-time (OFAT) sensitivity analysis is employed to assess the relative importance of undetermined parameters within each PV model. The SPO-based modeling framework enables high-accuracy PV performance prediction, and its application to sensitivity analysis can accurately identify key factors that lead to reduced computational cost and improved adaptability for integration with energy management systems and intelligent electric grids.

Keywords: photovoltaic system; improved metaheuristic algorithm; multi-diode model; parameter identification; sensitivity analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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