A Hybrid Genetic Algorithm- Particle Swarm Optimization Based Method for Estimating Parameters of Solar Photovoltaic Systems

References

[1] Abido, M. A. (2020). Hybrid algorithms in optimization for solar energy systems: Application to MPPT optimization. Renewable and Sustainable Energy Reviews, 125, 109799. https://doi.org/10.1016/j.rser.2020.109799 Link

[2] Arxiv. (2020). Enhancements in genetic algorithm performance through parallel and quantum computing. Retrieved from https://arxiv.org Link

[3] Arxiv. (2022). Particle swarm optimization for complex optimization problems. Retrieved from https://arxiv.org Link

[4] Chakrabarti, U., & Karmakar, B. K. (2024, January). A String Current Based Solar PV Array Fault Detection Technique. In 2024 IEEE 3rd International Conference on Control, Instrumentation, Energy & Communication (CIEC) (pp. 401-406). IEEE.

[5] Deb, K. (2020). Optimization using genetic algorithms: An introduction and applications. Wiley. https://www.wiley.com/en-us/Optimization+using+Genetic+Algorithms%3A+An+Introduction+and+Applications-p-9780471650196 Link

[6] Dorigo, M., & Stützle, T. (2019). Ant Colony Optimization. MIT Press. https://mitpress.mit.edu/9780262042192/ant-colony-optimization/ Link

[7] Frontiers in Chemistry. (2019). Improved GA approach for predicting nanoparticle structures. Retrieved from https://www.frontiersin.org Link

[8] Gupta, J., Hussain, A., Singla, M. K., Nijhawan, P., Haider, W., Kotb, H., & AboRas, K. M. (2023). Parameter estimation of different photovoltaic models using hybrid particle swarm optimization and gravitational search algorithm. Applied Sciences, 13(1), 249. https://doi.org/10.3390/app13010249(mdpi.com) Link

[9] Gusain, R. (2018). Optimal sizing and energy management of PV, wind, and battery systems using a hybrid GA-PSO algorithm. Energy, 154, 581–589. https://doi.org/10.1016/j.energy.2018.04.098 Link

[10] Khan, Z. U., Khan, A. D., Khan, K., Al Khatib, S. A. K., Khan, S., Khan, M. Q., & Ullah, A. (2024). A review of degradation and reliability analysis of a solar PV module. IEEe Access, 12, 185036-185056.s

[11] Kumar, V., Gupta, N., Yadav, A., Kumar, N., Verma, A., Kumar, A., ... & Jain, V. K. (2024). Enhancing solar photovoltaic system efficiency: recent progress on its cooling techniques. International Journal of Thermophysics, 45(8), 113.

[12] Liang, H., Shen, J., Yip, H. L., Fang, M. M., & Dong, L. (2024). Unleashing the green potential: Assessing Hong Kong's building solar PV capacity. Applied Energy, 369, 123567.

[13] Nature. (2024). Hybrid particle swarm optimization for improved convergence. Retrieved from https://www.nature.com Link

[14] PMC. (2023). Particle swarm optimization in medical applications. Retrieved from https://pmc.ncbi.nlm.nih.gov Link

[15] ResearchGate. (2021). Advances in PSO: Balancing exploration and exploitation. Retrieved from https://www.researchgate.net Link

[16] Rezki, M., Bensaid, S., Griche, I., & Houassine, H. (2020). Five PV model parameters determination through PSO and genetic algorithm: A comparative study. In Smart Energy Empowerment in Smart and Resilient Cities (pp. 11–23). Springer. https://doi.org/10.1007/978-3-030-37207-1_2 Link

[17] Saini, K., Saini, M., Kumar, A., & Saini, D. K. (2025). Performance analysis and optimization in renewable energy systems: a bibliometric review. Discover Applied Sciences, 7(3), 1-25.

[18] Saini, V., & Singal, S. K. (2024). Design and optimization of an off-grid integrated renewable energy system for remote rural electrification in India. Microsystem Technologies, 30(9), 1201-1215.

[19] Saravanan, C., & Panneerselvam, M. A. (2013). A comprehensive analysis for extracting single diode PV model parameters by hybrid GA-PSO algorithm. International Journal of Computer Applications, 78(8). https://doi.org/10.5120/13509-1265 Link

[20] Shao, S., Chen, H., & Yang, Y. (2019). A hybrid GAPSO algorithm for maximum power point tracking in solar systems. Energy Conversion and Management, 191, 352–364. https://doi.org/10.1016/j.enconman.2019.03.027 Link

[21] Singh, R. K., Tripathi, A., & Yadav, B. (2025). Development and Validation of Novel Fault Detection Algorithms for Deployed PV Systems. Research Journal of Engineering Technology and Medical Sciences (ISSN: 2582-6212), 8(02).

[22] Springer. (2021). Particle swarm optimization in machine learning applications. Retrieved from https://link.springer.com Link

[23] Wang, Y., Lee, S., Li, C., Umair, M., & Yakhyaeva, I. (2024). Techno-economic evaluation of solar photovoltaic power production in China for sustainable development and the environment. Environment, Development and Sustainability, 1-30.

[24] Zhao, B., Zhao, Z., Huang, M., Zhang, X., Li, Y., & Wang, R. (2021). Model predictive control of solar pv-powered ice-storage air-conditioning system considering forecast uncertainties. IEEE Transactions on Sustainable Energy, 12(3), 1672-1683.

@article{1713735,
      author = {Egbule Godwin Chimeuche, Anamonye Uzonna Gabriel, Gbigbidje Favour Peter, Emmanuel Ubiomo Ubeku},
      title = {A Hybrid Genetic Algorithm- Particle Swarm Optimization Based Method for Estimating Parameters of Solar Photovoltaic Systems},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {7},
      pages = {2543-2554},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1713735.pdf},
      abstract = {The present work investigates the modeling and parameter identification of solar photovoltaic systems using a hybrid optimization framework that integrates Genetic Algorithm and Particle Swarm Optimization methods. Solar photovoltaic systems are critical in the search for renewable energy sources, and their performances are highly dependent on environmental conditions like temperature, irradiance, and partial shading. The development of an accurate model is vital to ensure the efficient design and optimal performance of such systems. Conventional techniques of estimating parameters are not effective in handling nonlinearities and sensitivity associated with PV systems. The hybrid GA-PSO algorithm combines the global search capability of the Genetic Algorithm with fast convergence properties of Particle Swarm Optimization to conduct an efficient optimization of key parameters of the PV system, including photocurrent, series resistance, shunt resistance, and the diode ideality factor. The key focus of this paper was aimed at the enhancement of accuracy in the estimation of solar PV systems' parameters under varying environmental conditions, thus leading to better PV performance and efficiency. The research methodology involved simulating the solar PV system using MATLAB, optimizing key parameters using a hybrid GA-PSO algorithm, and model validation with experimental data. Optimized parameters are further utilized to develop current-voltage (I-V) and power-voltage (P-V) characteristics for different conditions of irradiance and temperature.},
      keywords = {Solar Photovoltaic, Genetic Algorithm, Particle Swarm Optimization, Parameter Estimation, Solar Irradiance.},
      month = {January},
      doi = {https://doi.org/10.64388/IREV9I7-1713735}
  }