| Issue |
E3S Web Conf.
Volume 692, 2026
3rd International Conference on Intelligent and Sustainable Power and Energy Systems (ISPES 2025)
|
|
|---|---|---|
| Article Number | 01012 | |
| Number of page(s) | 9 | |
| Section | Energy | |
| DOI | https://doi.org/10.1051/e3sconf/202669201012 | |
| Published online | 04 February 2026 | |
Enhanced Parameter Estimation Approach for Modeling of Three-Diode Solar Photovoltaic System
School of Technology, Woxsen University, Hyderabad, Telangana
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
Parameter estimation of the photovoltaic (PV) models is required to be accurate to attain high efficacy and reliability of solar energy systems. This paper introduces a more advanced model construction and optimization method of PV parameter estimation with three-diode model using Teaching-Learning-Based Optimization (TLBO) optimization algorithm. The three-diode model (TDM) has the benefit of being able to model both complex recombination and leakage processes in the solar cell with better accuracy under different irradiance and temperature conditions than single or two-diode models. TLBO was inspired by the teaching-learning process of classroom and was proposed to estimate the model parameters. The process is designed to reduce the root mean square error RMSE between the measured and simulated I-V data of current voltage. The proposed TLBO-based algorithm is implemented on the RTC France solar cell data and is found to be superior in convergence and accuracy over the algorithms used as benchmarking algorithms. The results confirm that TLBO is an efficient and reliable tool in the PV system modeling and optimization and can be applied to find a balance between global exploration and local exploitation without any algorithm-specific control parameters.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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