Optimization Technique for Parameter Estimation in Solar Photovoltaic Systems Using Nanomaterials
| Authors | Sandip R. Thorat1, P. William2 , Laxmikant S. Dhamande1, Prasad M. Patare1, Yogeesh N3, Tarun Dhar Diwan4 , Apurv Verma5 |
| Affiliations |
1Department of Mechanical Engineering, Sanjivani College of Engineering, Kopargaon, MH, India 2Department of Information Technology, Sanjivani College of Engineering, Kopargaon, MH, India 3Department of Mathematics, Government First Grade College, Tumkur, Karnataka, India 4Controller of Examination (COE), Atal Bihari Vajpayee University, Bilaspur, India5Department of Computer Science and Engineering, SSIPMT, Raipur |
| Е-mail | tarunctech@gmail.com |
| Issue | Volume 17, Year 2025, Number 4 |
| Dates | Received 23 April 2025; revised manuscript received 20 August 2025; published online 29 August 2025 |
| Citation | Sandip R. Thorat, P. William, Laxmikant S. Dhamande, et al., J. Nano- Electron. Phys. 17 No 4, 04030 (2025) |
| DOI | https://doi.org/10.21272/jnep.17(4).04030 |
| PACS Number(s) | 07.05.Dz, 88.40.H – |
| Keywords | Intelligent Ant Colony Optimization (IACO), Scalable Cuckoo Search Algorithm (ICSA), Updated One-Diode, Updated Two-Diode. |
| Annotation |
Nanomaterial integration in solar photovoltaic systems improves the efficiency of solar photovoltaic systems through better light capture and charge carrier transportation. To estimate parameters for these systems, improved optimization can fine-tune the efficiency of energy conversion and improve system robustness. This study aims to develop an optimization technique for accurate parameter estimation in solar photovoltaic systems using nanomaterials. This approach seeks to enhance the efficiency and performance of solar cells by leveraging advanced optimization algorithms. Intelligent Ant Colony Optimization (IACO) helps to estimate the parameters of solar photovoltaic systems with a more accurate simulation of the behavior of ants and their optimization functions to nanomaterials for energy production. For realizing enhanced accuracy and convergence in solar photovoltaic parameters, the Scalable Cuckoo Search Algorithm (SCSA) is used in the light of cuckoo nesting. The research uses two primary photovoltaic models (Updated One-Diode, and Updated Two-Diode) to assess the effects of nanomaterial integration. The integration of nanomaterials with the hybrid IACO-SCSA optimization led to significant improvements in solar cell efficiency. The study showed that the UODM and UTDM, while optimizing using hybrid IACO-SCSA, outperformed better than other models, such as SSE (Sum of Squared Error), RMSE (Root Mean Squared Error), and MAE (Mean Absolute Error). |
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List of References |
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