Photovoltaic Cell Parameter Estimation Using Moth-Flame Optimization Algorithm
DOI:
10.29303/jppipa.v11i7.10892Published:
2025-07-25Downloads
Abstract
Estimation of photovoltaic cell parameters from experimental data is an important part of photovoltaic system performance modeling and optimization. This study aims to estimate the parameters of photovoltaic cells. The photovoltaic models used are the one-diode model (ODM) and the two-diode model (TDM). Optimization is performed using the moth-flame optimization (MFO) algorithm. The root mean square error (RMSE) method is applied to determine the accuracy of the estimated parameters. Experimental results show that the MFO algorithm is able to obtain photovoltaic cell parameters with a high level of accuracy, both in ODM and TDM. The current-voltage (I-V) and power-voltage (P-V) curves between the measured and estimated data also show a very good match. In addition, the optimization algorithm outperforms most metaheuristic algorithms applied in photovoltaic cell parameter determination. Thus, the MFO algorithm is suitable to be applied in determining the photovoltaic cell parameters.
Keywords:
Metaheuristic algorithm One-diode model Photovoltaic parameters Two-diode modelReferences
Abdel-Basset, M., Mohamed, R., Hezam, I. M., Sallam, K. M., & Hameed, I. A. (2024). Parameters identification of photovoltaic models using Lambert W-function and Newton-Raphson method collaborated with AI-based optimization techniques: A comparative study. Expert Systems with Applications, 255(PD), 124777. https://doi.org/10.1016/j.eswa.2024.124777
Abdulrazzaq, A. K., Bognár, G., & Plesz, B. (2022). Accurate method for PV solar cells and modules parameters extraction using I–V curves. Journal of King Saud University - Engineering Sciences, 34(1). https://doi.org/10.1016/j.jksues.2020.07.008
Adak, S., Cangi, H., Yilmaz, A. S., & Arifoglu, U. (2023). Development software program for extraction of photovoltaic cell equivalent circuit model parameters based on the Newton–Raphson method. In Journal of Computational Electronics (Vol. 22, Issue 1). https://doi.org/10.1007/s10825-022-01969-8
Bakır, H. (2023). Comparative performance analysis of metaheuristic search algorithms in parameter extraction for various solar cell models. Environmental Challenges, 11. https://doi.org/10.1016/j.envc.2023.100720
Bălăceanu, M., Popa, N. S., Nae, V., Târhoacă, M. C., & Popescu, M. O. (2024). The Influence of Environmental Factors on a Photovoltaic Panel–PVsyst Simulation. EEA - Electrotehnica, Electronica, Automatica, 72(2), 03–12. https://doi.org/10.46904/eea.24.72.2.1108001
Beigi, A. M., & Maroosi, A. (2018). Parameter identification for solar cells and module using a Hybrid Firefly and Pattern Search Algorithms. Solar Energy, 171. https://doi.org/10.1016/j.solener.2018.06.092
Ćalasan, M., Radonjić, I., Micev, M., Petronijević, M., & Pantić, L. (2024). Voltage root mean square error calculation for solar cell parameter estimation: A novel g-function approach. Heliyon, 10(18). https://doi.org/10.1016/j.heliyon.2024.e37887
Çelik, E., Karayel, M., Maden, D., Abdel-salam, M., Öztürk, N., Kaplan, O., Tejani, G. G., Sharma, S. K., & Baljon, M. (2025). Reconfigured single- and double- diode models for improved modelling of solar cells / modules. Scientific Reports, 1–19.
Chen, X., Yue, H., & Yu, K. (2019). Perturbed stochastic fractal search for solar PV parameter estimation. Energy, 189. https://doi.org/10.1016/j.energy.2019.116247
Dkhichi, F. (2023). Parameter extraction of photovoltaic module model by using Levenberg-Marquardt algorithm based on simulated annealing method. Journal of Computational Electronics, 22(4). https://doi.org/10.1007/s10825-023-02058-0
Durango-Flórez, M., González-Montoya, D., Trejos-Grisales, L. A., & Ramos-Paja, C. A. (2022). PV Array Reconfiguration Based on Genetic Algorithm for Maximum Power Extraction and Energy Impact Analysis. Sustainability (Switzerland), 14(7). https://doi.org/10.3390/su14073764
El Ainaoui, K., Zaimi, M., & Assaid, E. M. (2023). Innovative approaches to extract double-diode model physical parameters of a PV module serving outdoors under real-world conditions. Energy Conversion and Management, 292. https://doi.org/10.1016/j.enconman.2023.117365
Gao, X., Cui, Y., Hu, J., Xu, G., Wang, Z., Qu, J., & Wang, H. (2018). Parameter extraction of solar cell models using improved shuffled complex evolution algorithm. Energy Conversion and Management, 157. https://doi.org/10.1016/j.enconman.2017.12.033
Hamid, N. F. A., Rahim, N. A., & Selvaraj, J. (2016). Solar cell parameters identification using hybrid Nelder-Mead and modified particle swarm optimization. Journal of Renewable and Sustainable Energy, 8(1). https://doi.org/10.1063/1.4941791
Jamadi, M., Merrikh-Bayat, F., & Bigdeli, M. (2016). Very accurate parameter estimation of single- and double-diode solar cell models using a modified artificial bee colony algorithm. International Journal of Energy and Environmental Engineering, 7(1). https://doi.org/10.1007/s40095-015-0198-5
Jannah, N., Gunawan, T. S., Yusoff, S. H., Hanifah, M. S. A., & Sapihie, S. N. M. (2024). Recent Advances and Future Challenges of Solar Power Generation Forecasting. IEEE Access, 12(November), 168904–168924. https://doi.org/10.1109/ACCESS.2024.3496120
Kang, T., Yao, J., Jin, M., Yang, S., & Duong, T. (2018). A novel improved cuckoo search algorithm for parameter estimation of photovoltaic (PV) models. Energies, 11(5). https://doi.org/10.3390/en11051060
Kwakye, B. D., Li, Y., Mohamed, H. H., Baidoo, E., & Asenso, T. Q. (2024). Particle guided metaheuristic algorithm for global optimization and feature selection problems[Formula presented]. Expert Systems with Applications, 248. https://doi.org/10.1016/j.eswa.2024.123362
Li, S., Gong, W., Yan, X., Hu, C., Bai, D., & Wang, L. (2019). Parameter estimation of photovoltaic models with memetic adaptive differential evolution. Solar Energy, 190. https://doi.org/10.1016/j.solener.2019.08.022
Li, S., Gong, W., Yan, X., Hu, C., Bai, D., Wang, L., & Gao, L. (2019). Parameter extraction of photovoltaic models using an improved teaching-learning-based optimization. Energy Conversion and Management, 186. https://doi.org/10.1016/j.enconman.2019.02.048
Liao, Z., Chen, Z., & Li, S. (2020). Parameters Extraction of Photovoltaic Models Using Triple-Phase Teaching-Learning-Based Optimization. IEEE Access, 8. https://doi.org/10.1109/ACCESS.2020.2984728
Lin, H., Wang, G., Su, Q., Han, C., Xue, C., Yin, S., Fang, L., Xu, X., & Gao, P. (2024). Unveiling the mechanism of attaining high fill factor in silicon solar cells. Progress in Photovoltaics: Research and Applications, 32(6). https://doi.org/10.1002/pip.3775
Lin, P., Cheng, S., Yeh, W., Chen, Z., & Wu, L. (2017). Parameters extraction of solar cell models using a modified simplified swarm optimization algorithm. Solar Energy, 144. https://doi.org/10.1016/j.solener.2017.01.064
Lo, W. L., Chung, H. S. H., Hsung, R. T. C., Fu, H., & Shen, T. W. (2024). PV Panel Model Parameter Estimation by Using Particle Swarm Optimization and Artificial Neural Network. Sensors, 24(10). https://doi.org/10.3390/s24103006
Morcillo, J., Castaneda, M., Jímenez, M., Zapata, S., Dyner, I., & Aristizabal, A. J. (2022). Assessing the speed, extent, and impact of the diffusion of solar PV. Energy Reports, 8. https://doi.org/10.1016/j.egyr.2022.06.099
Nfaoui, M., Ihfa, F. E., Bougtaib, A., Harfouf, A. El, Hayani-Mounir, S., Bennai, M., & El-Hami, K. (2024). Comprehensive modeling and simulation of photovoltaic system performance by using matlab/simulink: integrating dynamic meteorological parameters for enhanced accuracy. Journal of Umm Al-Qura University for Applied Sciences, 0123456789. https://doi.org/10.1007/s43994-024-00175-5
Oliva, D., Abd El Aziz, M., & Ella Hassanien, A. (2017). Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm. Applied Energy, 200. https://doi.org/10.1016/j.apenergy.2017.05.029
Pindado, S., Roibas-Millan, E., Cubas, J., Alvarez, J. M., Alfonso-Corcuera, D., Cubero-Estalrrich, J. L., Gonzalez-Estrada, A., Sanabria-Pinzon, M., & Jado-Puente, R. (2021). Simplified Lambert W-Function Math Equations When Applied to Photovoltaic Systems Modeling. IEEE Transactions on Industry Applications, 57(2). https://doi.org/10.1109/TIA.2021.3052858
Saadaoui, D., Elyaqouti, M., Assalaou, K., Ben hmamou, D., & Lidaighbi, S. (2021). Parameters optimization of solar PV cell/module using genetic algorithm based on non-uniform mutation. In Energy Conversion and Management: X (Vol. 12). https://doi.org/10.1016/j.ecmx.2021.100129
Sakthivel, S. S., Gokulakrishnan, L., Arunachaleashwer, S., Lokesh, V., & Venkadesan, A. (2023). Parameter Estimation of a Single Diode Model of a PV Panel using Gauss-Seidel Method: An Experimental Validation. 2023 International Conference for Advancement in Technology, ICONAT 2023. https://doi.org/10.1109/ICONAT57137.2023.10080499
Tifidat, K., Maouhoub, N., Askar, S. S., & Abouhawwash, M. (2023). Numerical procedure for accurate simulation of photovoltaic modules performance based on the identification of the single-diode model parameters. Energy Reports, 9. https://doi.org/10.1016/j.egyr.2023.04.378
Tifidat, K., Maouhoub, N., Benahmida, A., & Ezzahra Ait Salah, F. (2022). An accurate approach for modeling I-V characteristics of photovoltaic generators based on the two-diode model. Energy Conversion and Management: X, 14. https://doi.org/10.1016/j.ecmx.2022.100205
Wang, J., Wei, H., Dou, S., Gillbanks, J., & Zhao, X. (2024). Active Disturbance Rejection Control Based on an Improved Topology Strategy and Padé Approximation in LCL-Filtered Photovoltaic Grid-Connected Inverters. Applied Sciences (Switzerland), 14(23). https://doi.org/10.3390/app142311133
Xiong, G., Zhang, J., Shi, D., & Yuan, X. (2019). Application of Supply-Demand-Based Optimization for Parameter Extraction of Solar Photovoltaic Models. Complexity, 2019. https://doi.org/10.1155/2019/3923691
Xiong, G., Zhang, J., Yuan, X., Shi, D., & He, Y. (2018). Application of symbiotic organisms search algorithm for parameter extraction of solar cell models. Applied Sciences (Switzerland), 8(11). https://doi.org/10.3390/app8112155
Yu, K., Qu, B., Yue, C., Ge, S., Chen, X., & Liang, J. (2019). A performance-guided JAYA algorithm for parameters identification of photovoltaic cell and module. Applied Energy, 237. https://doi.org/10.1016/j.apenergy.2019.01.008
Yuan, S., Ji, Y., Chen, Y., Liu, X., & Zhang, W. (2023). An Improved Differential Evolution for Parameter Identification of Photovoltaic Models. Sustainability (Switzerland), 15(18). https://doi.org/10.3390/su151813916
License
Copyright (c) 2025 Fitriah Husin, Ayong Hiendro
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with Jurnal Penelitian Pendidikan IPA, agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution 4.0 International License (CC-BY License). This license allows authors to use all articles, data sets, graphics, and appendices in data mining applications, search engines, web sites, blogs, and other platforms by providing an appropriate reference. The journal allows the author(s) to hold the copyright without restrictions and will retain publishing rights without restrictions.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in Jurnal Penelitian Pendidikan IPA.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
