COOT OPTIMIZATION ALGORITHM FOR PARAMETER ESTIMATION OF PHOTOVOLTAIC MODEL

Hussam K. Rushdi, Fawzi M. Al-Naima

Abstract


Because of the technical and environmental advantages of many solar energy sources, their use has recently been rapidly rising. The extraction of the unknown parameters in photovoltaic models is one of the key challenges in the modeling and simulation of solar energy sources. To satisfy the behavior of the solar photovoltaic (SPV) cells, the Single-Diode Model (SDM) is recommended as a more dependable modeling method. In this study, we applied the recently introduced meta-heuristic optimization method that inspires the behavior of the swarm of birds called Coot.  This Coot Algorithm is used to estimate the unknown parameters of an SPV cell/module at 33 ºC. Simulation results of this study were compared with other nine different previous Optimization Algorithms, which are Moth Flame Optimization (MFO), Dragonfly Algorithm (DA), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), Ant Lion Optimization (ALO), Harris Hawk Optimization (HHO), Hybrid of Particle Swarm Optimization and Grey Wolf Optimization (PSOGWO), Marine Predator Algorithm (MPA), and African Vultures Optimization Algorithm (AVOA). The obtained results of this comparison showed that the Coot algorithm outperformed the previous algorithms in terms of the root mean square error (RMSE) and the degree of convergence between the power versus voltage curve and the current versus voltage curve compared with the measured data. Moreover, the results confirm that the Coot optimization algorithm is favorable in reducing time and improving accuracy.

Keywords


solar photovoltaic, Coot algorithm, parameter estimation, single-diode model, solar energy

Full Text:

PDF

References


Abbassi, R., Chebbi S. (2012). Energy management strategy for a grid-connected wind-solar hybrid system with battery storage: Policy for optimizing conventional energy generation., 3979–3990.

AlHajri, M. F., El-Naggar, K. M., AlRashidi, M. R., & Al-Othman, A. K. (2012). Optimal extraction of solar cell parameters using pattern search. Renewable Energy, 44, 238–245. doi: 10.1016/j.renene.2012.01.082

Awan, A. B., & Khan, Z. A. (2014). Recent progress in renewable energy – Remedy of energy crisis in Pakistan. Renewable and Sustainable Energy Reviews, 33, 236–253. doi: 10.1016/j.rser.2014.01.089

Chellaswamy, C., & Ramesh, R. (2016). Parameter extraction of solar cell models based on adaptive differential evolution algorithm. Renewable Energy, 97, 823–837. doi: 10.1016/j.renene.2016.06.024

Chen, Z., Wu, L., Lin, P., Wu, Y., & Cheng, S. (2016). Parameters identification of photovoltaic models using hybrid adaptive Nelder-Mead simplex algorithm based on eagle strategy. Applied Energy, 182, 47–57. doi: 10.1016/j.apenergy.2016.08.083

Darmansyah, & Robandi, I. (2017). Photovoltaic parameter estimation using Grey Wolf Optimization. In 2017 3rd International Conference on Control, Automation and Robotics (ICCAR) (pp. 593–597). IEEE. doi: 10.1109/ICCAR.2017.7942766

Easwarakhanthan, T., Bottin, J., Bouhouch, I., & Boutrit, C. (1986). Nonlinear Minimization Algorithm for Determining the Solar Cell Parameters with Microcomputers. International Journal of Solar Energy, 4(1), 1–12. doi: 10.1080/01425918608909835

Hanger, S., Komendantova, N., Schinke, B., Zejli, D., Ihlal, A., & Patt, A. (2016). Community acceptance of large-scale solar energy installations in developing countries: Evidence from Morocco. Energy Research & Social Science, 14, 80–89. doi: 10.1016/j.erss.2016.01.010

Irmak, E., Ayaz, M. S., Gok, S. G., & Sahin, A. B. (2014). A survey on public awareness towards renewable energy in Turkey. In 2014 International Conference on Renewable Energy Research and Application (ICRERA) (pp. 932–937). IEEE. doi: 10.1109/ICRERA.2014.7016523

Isa, Z. M., Nayan, N. M., Kajaan, N. A. M., & Arshad, M. H. (2020). A Dragonfly Algorithm Application: Optimizing Solar Cell Single Diode Model Parameters. Journal of Physics: Conference Series, 1432(1), 012041. doi: 10.1088/1742-6596/1432/1/012041

Jha, S. Kr., Bilalovic, J., Jha, A., Patel, N., & Zhang, H. (2017). Renewable energy: Present research and future scope of Artificial Intelligence. Renewable and Sustainable Energy Reviews, 77, 297–317. doi: 10.106/j.rser.2017.04.018

Kanimozhi, G., & Kumar, H. (2018). Modeling of solar cell under different conditions by Ant Lion Optimizer with LambertW function. Applied Soft Computing, 71, 141–151. doi: 10.1016/j.asoc.2018.06.025

Kumar, C., & Mary, D. M. (2021). Parameter estimation of three-diode solar photovoltaic model using an Improved-African Vultures optimization algorithm with Newton–Raphson method. Journal of Computational Electronics, 20(6), 2563–2593. doi: 10.1007/s10825-021-01812-6

Lau, L. C., Lee, K. T., & Mohamed, A. R. (2012). Global warming mitigation and renewable energy policy development from the Kyoto Protocol to the Copenhagen Accord—A comment. Renewable and Sustainable Energy Reviews, 16(7), 5280–5284. doi: 10.1016/j.rser.2012.04.006

Louzazni, M., & Aroudam, E. H. (2015). An analytical mathematical modeling to extract the parameters of solar cell from implicit equation to explicit form. Applied Solar Energy, 51(3), 165–171. doi: 10.3103/S0003701X15030068

Mcelroy, M. B., & Chen, X. (2017). Wind and solar power in the United States: status and prospects. CSEE Journal of Power and Energy Systems, 3(1), 1–6. doi: 10.17775/CSEEJPES.2017.0002

Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228–249. doi: 10.1016/j.knosys.2015.07.006

Naruei, I., & Keynia, F. (2021). A new optimization method based on COOT bird natural life model. Expert Systems with Applications, 183, 115352. doi: 10.1016/j.eswa.2021.115352

Premkumar, M., Sowmya, R., Umashankar, S., & Jangir, P. (2021). Extraction of uncertain parameters of single-diode photovoltaic module using hybrid particle swarm optimization and grey wolf optimization algorithm. Materials Today: Proceedings, 46, 5315–5321. doi: 10.1016/j.matpr.2020.08.784

Sattar, M. A. E., Sumaiti, A. A., Ali, H., & Diab, A. A. Z. (2021). Marine predators algorithm for parameters estimation of photovoltaic modules considering various weather conditions. Neural Computing and Applications, 33(18), 11799–11819. doi: 10.1007/s00521-021-05822-0

Sharma, A., Saxena, A., Shekhawat, S., Kumar, R., & Mathur, A. (2021). Solar Cell Parameter Extraction by Using Harris Hawks Optimization Algorithm (pp. 349–379). doi: 10.1007/978-981-15-5495-7_20

Tao, Y., Bai, J., Pachauri, R. K., & Sharma, A. (2020). Parameter extraction of photovoltaic modules using a heuristic iterative algorithm. Energy Conversion and Management, 224, 113386. doi: 10.1016/j.enconman.2020.113386

Wu, Z., Tazvinga, H., & Xia, X. (2015). Demand side management of photovoltaic-battery hybrid system. Applied Energy, 148, 294–304. doi: 10.1016/j.apenergy.2015.03.109

Xiong, G., Zhang, J., Shi, D., & He, Y. (2018). Parameter extraction of solar photovoltaic models using an improved whale optimization algorithm. Energy Conversion and Management, 174, 388–405. doi: 10.1016/j.enconman.2018.08.053


Refbacks

  • There are currently no refbacks.