Optimal parameters extraction for photovoltaic models utilizing an artificial rabbit optimizer incorporating swarm-elite learning mechanism’s Levy flight strategy

Accurate extraction of unknown parameter values in photovoltaic (PV) models contributes to optimizing and enhancing the energy conversion efficiency of PV systems. The current-voltage relationship in PV models is nonlinear, and the parameters change with varying illumination and temperature conditio...

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Veröffentlicht in:Computers & electrical engineering Jg. 127; S. 110582
Hauptverfasser: Wang, Wentao, Tian, Jun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.10.2025
Schlagworte:
Artificial rabbit optimization algorithm
Metaheuristic algorithm
Photovoltaic model
Renewable energy
Renewable energy
Metaheuristic algorithm
Artificial rabbit optimization algorithm
Photovoltaic model
ISSN:0045-7906
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Zusammenfassung:Accurate extraction of unknown parameter values in photovoltaic (PV) models contributes to optimizing and enhancing the energy conversion efficiency of PV systems. The current-voltage relationship in PV models is nonlinear, and the parameters change with varying illumination and temperature conditions. Consequently, parameter extraction becomes a complex and challenging optimization task. Mathematical modeling transforms PV model parameter extraction into an optimization problem. This paper proposes a swarm-elite learning mechanism's Levy flight and individual Mutation enhanced Artificial Rabbit Optimization algorithm (LMARO). The aim is to apply this algorithm to solve various PV model parameter extraction problems. During LMARO's detour foraging phase, the combination of elite individuals' high-quality information and the jumping characteristics of Levy flight is employed to enhance the algorithm's global search capability. In LMARO's random hiding phase, individuals are subject to mutation perturbations based on their constituent dimensions, thereby augmenting the algorithm's population diversity. The comparison experiments of the ten CEC2017 functions show that LMARO performs excellently on specific optimization problems. The performance of LMARO is thoroughly evaluated in five PV models of different complexity and compared with ten advanced metaheuristics and the Levenberg-Marquardt algorithm. The results show that LMARO achieves average Root Mean Square Errors (RMSE) of 9.8670E-04, 9.8798E-04, 2.4446E-03, 1.7373E-03 and 1.6334E-02 in PV models of Single Diode Model (SDM), Double Diode Model (DDM), Photowatt-PWP201, STM6-40/36 and STP6-120/36, respectively. In addition, LMARO shows excellent performance in terms of solution accuracy and convergence speed.
ISSN:0045-7906
DOI:10.1016/j.compeleceng.2025.110582