article · Electronics
Accurately determining unknown parameters in photovoltaic modules is essential for modelling solar cell behaviour under different equivalent circuits. A modified version of the physics-inspired Rime Optimization Algorithm incorporates a polynomial differential learning operator to introduce non-linearities into the search process. This enhancement improves convergence speed, adaptability, and global search capability when estimating parameters for both single-diode and double-diode models. When evaluated through simulations on commercial hardware, including the STM6-40/36 module and the R.T.C. France solar cell, the modified algorithm outperforms the conventional method and contemporary published techniques. Performance gains reach 1.16 percent and 18.45 percent for the single-diode model across the two modules, and 1.14 percent and 50.42 percent for the double-diode model. The method demonstrates robust optimization performance across distinct solar cell configurations.
Solar energy systems rely on precise mathematical models to predict electrical performance and improve power output. Estimating unseen physical parameters inside solar cells often challenges standard computational tools. Refining optimization algorithms to extract these values more accurately ensures photovoltaic systems can be analysed and designed with greater precision, supporting the efficient operation and control of renewable power installations.
The method provides a computational tool for photovoltaic engineers and system designers requiring accurate parameter extraction for solar cells and modules. Because the reported evidence relies entirely on simulations of commercial components, the technology is at an applied research stage. Practical commercial use would require embedding the algorithm into commercial solar modelling software or operational monitoring toolkits.
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A recent optimization algorithm, the Rime Optimization Algorithm (RIME), was developed to efficiently utilize the physical phenomenon of rime-ice growth. It simulates the hard-rime and soft-rime processes, constructing the mechanisms of hard-rime puncture and soft-rime search. In this study, an enhanced version, termed Modified RIME (MRIME), is introduced, integrating a Polynomial Differential Learning Operator (PDLO). The incorporation of PDLO introduces non-linearities to the RIME algorithm, enhancing its adaptability, convergence speed, and global search capability compared to the conventional RIME approach. The proposed MRIME algorithm is designed to identify photovoltaic (PV) module characteristics by considering diverse equivalent circuits, including the One-Diode Model (ONE-DM) and Two-Diode Model TWO-DM, to determine the unspecified parameters of the PV. The MRIME approach is compared to the conventional RIME method using two commercial PV modules, namely the STM6-40/36 module and R.T.C. France cell. The simulation results are juxtaposed with those from contemporary algorithms based on published research. The outcomes related to recent algorithms are also compared with those of the MRIME algorithm in relation to various existing studies. The simulation results indicate that the MRIME algorithm demonstrates substantial improvement rates for the STM6-40/36 module and R.T.C. France cell, achieving 1.16% and 18.45% improvement for the ONE-DM, respectively. For the TWO-DM, it shows significant improvement rates for the two modules, reaching 1.14% and 50.42%, respectively. The MRIME algorithm, in comparison to previously published results, establishes substantial superiority and robustness.
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DOI: 10.3390/electronics13091611
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