Novel DNMRIME Algorithm Achieves Unprecedented Accuracy in Photovoltaic Parameter Estimation, Advancing Solar Energy Modeling
Key Insights
Researchers have developed DNMRIME, a novel metaheuristic algorithm, significantly enhancing the accuracy of photovoltaic (PV) cell parameter estimation.
DNMRIME integrates a dynamic multi-dimensional random mechanism and Nelder–Mead simplex, improving convergence and enabling escape from local optima.
The algorithm demonstrated superior performance against 14 other metaheuristic algorithms, achieving exceptionally low RMSE values across various PV models.
DNMRIME successfully extracted parameters for commercial PV modules under varying conditions, with simulation data closely matching actual experimental results.
Researchers have developed a novel metaheuristic algorithm, DNMRIME, which significantly enhances the accuracy and efficiency of photovoltaic (PV) cell parameter estimation, a critical step for optimizing solar energy system performance. Published in Scientific Reports, this advanced algorithm addresses the long-standing challenge of precisely characterizing PV models, which exhibit complex nonlinear relationships influenced by varying light intensity, temperature, and intricate cell structures. The breakthrough promises to improve the reliability of PV system simulations, a key factor for maximizing energy yield and accelerating solar energy deployment.
The DNMRIME algorithm, an enhanced version of the recently introduced Rime Optimization Algorithm (RIME), integrates a dynamic multi-dimensional random mechanism (DMRM) with the Nelder–Mead simplex (NMs) method. DMRM improves RIME's convergence accuracy through random non-periodic convergence, while NMs accelerate the process and enable DNMRIME to effectively escape local optima, a common pitfall in complex optimization problems. This hybrid approach allows DNMRIME to perform exceptionally well on hybrid and composite functions, crucial for accurately modeling the transcendental equations inherent in PV systems.
To validate its performance, DNMRIME underwent rigorous qualitative analysis and ablation studies on the CEC 2017 benchmark. Comparative evaluations against 14 well-known metaheuristic algorithms, including several champion algorithms, demonstrated DNMRIME's superior efficacy. The Wilcoxon signed-rank test results confirmed DNMRIME's top ranking among the tested algorithms, underscoring its robust optimization capabilities.
In practical applications for PV parameter extraction, DNMRIME achieved remarkable accuracy across various solar cell models. For the Single-Diode Model (SDM), Double-Diode Model (DDM), Triple-Diode Model (TDM), and the general PV model, the algorithm yielded mean Root Mean Square Error (RMSE) values of 0.000986, 0.000983, 0.000984, and 0.002425, respectively. These low RMSE values indicate a high degree of precision in parameter identification.
Furthermore, DNMRIME's effectiveness was verified under diverse environmental conditions. The algorithm successfully extracted parameters for commercial PV modules from three manufacturers—KC200GT, ST40, and SM55—under varying temperature and irradiation levels. The simulation data generated using DNMRIME’s extracted parameters closely matched actual experimental data, confirming its practical utility and reliability in real-world scenarios. This study’s introduction of DMRM and DNMRIME represents a significant contribution, offering a highly efficient and practical tool for photovoltaic parameter extraction, distinguishing it from previous research that often lacked comprehensive performance analysis and broad model evaluation.