Estimation of Parameters of One-diode and Two-diode Photovoltaic Models: A Chaotic Gravitational Search Algorithm based Approach

References

• Abdel-Basset, M., R. Mohamed, S. Mirjalili, R. K. Chakrabortty, and M. J. Ryan. 2020. Solar photovoltaic parameter estimation using an improved equilibrium optimizer. Solar Energy 209:694–708. doi:10.1016/j.solener.2020.09.032.
   Web of Science ®Google Scholar
• Aleem, S. A., S. M. S. Hussain, and T. S. Ustun. 2020. A review of strategies to increase pv penetration level in smart grids. Energies 13 (3):636. doi:10.3390/en13030636.
   Web of Science ®Google Scholar
• Babu, B. C., and S. Gurjar. 2014. A novel simplified two-diode model of photovoltaic (pv) module. IEEE Journal of Photovoltaics 4 (4):1156–61. doi:10.1109/JPHOTOV.2014.2316371.
   Web of Science ®Google Scholar
• Badawy, M. O., and Y. Sozer. 2016. Power flow management of a grid tied PV-battery system for electric vehicles charging. IEEE Transactions on Industry Applications 53 (2):1347–57. doi:10.1109/TIA.2016.2633526.
   Web of Science ®Google Scholar
• Celik, A. N., and N. Acikgoz. 2007. Modelling and experimental verification of the operating current of mono-crystalline photovoltaic modules using four and five-parameter models. Applied Energy 84 (1):1–15. doi:10.1016/j.apenergy.2006.04.007.
   Web of Science ®Google Scholar
• Chen, H., S. Jiao, A. A. Heidari, M. Wang, X. Chen, and X. Zhao. 2019. An opposition-based sine cosine approach with local search for parameter estimation of photovoltaic models. Energy Conversion and Management 195:927–42. doi:10.1016/j.enconman.2019.05.057.
   Web of Science ®Google Scholar
• Chen, X., B. Xu, C. Mei, Y. Ding, and K. Li. 2018. Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation. Applied Energy 212:1578–88. doi:10.1016/j.apenergy.2017.12.115.
   Web of Science ®Google Scholar
• Chen, X., K. Yu, W. Du, W. Zhao, and G. Liu. 2016. Parameters identification of solar cell models using generalized oppositional teaching learning based optimization. Energy 99:170–80. doi:10.1016/j.energy.2016.01.052.
   Web of Science ®Google Scholar
• Chen, X., H. Yue, and K. Yu. 2019. Perturbed stochastic fractal search for solar PV parameter estimation. Energy 189:116247. doi:10.1016/j.energy.2019.116247.
   Web of Science ®Google Scholar
• Diab, A. A. Z., H. M. Sultan, T. D. Do, O. M. Kamel, and M. A. Mossa. 2020. Coyote optimization algorithm for parameters estimation of various models of solar cells and PV modules. Institute of Electrical and Electronics EngineersAccess 8:111102–40. doi:10.1109/ACCESS.2020.3000770.
   Google Scholar
• Diaz, P., R. Peña, J. Muñoz, C. A. Arias, and D. Sandoval. 2011. Field analysis of solar PV-based collective systems for rural electrification. Energy 36 (5):2509–16. doi:10.1016/j.energy.2011.01.043.
   Web of Science ®Google Scholar
• Espinet-Gonzalez, P., E. Barrigón, Y. Chen, G. Otnes, G. Vescovi, C. Mann, J. V. Lloyd, D. Walker, M. D. Kelzenberg, I. Aberg, et al. 2020. Nanowire solar cells: A new radiation hard PV technology for space applications. IEEE Journal of Photovoltaics. 10(2):502–07. doi:10.1109/JPHOTOV.2020.2966979.
   Web of Science ®Google Scholar
• Et-Torabi, K., I. Nassar-Eddine, A. Obbadi, Y. Errami, R. Rmaily, S. Sahnoun, M. Agunaou, and M. Agunaou. 2017. Parameters estimation of the single and double diode photovoltaic models using a Gauss–Seidel algorithm and analytical method: A comparative study. Energy Conversion and Management 148:1041–54. doi:10.1016/j.enconman.2017.06.064.
   Web of Science ®Google Scholar
• Ghosh, A., A. K. Ray, and O. Singh. 2021. Modelling of a GSA-based framework of an agent based system within parameter boundaries: applications to robotics and automatic voltage regulator systems. International Journal of Modelling, Identification and Control 39 (2):159–71. doi:10.1504/IJMIC.2021.123432.
   Web of Science ®Google Scholar
• Ghosh, A., O. Singh, A. K. Ray, and M. Nurujjaman. 2018. Gravitational search algorithm optimized state feedback load frequency controller. In 2018 15th IEEE India Council International Conference (INDICON), Coimbatore, India. 1–6.
   Google Scholar
• Houssein, E. H., M. E. Hosney, M. Elhoseny, D. Oliva, W. M. Mohamed, and M. Hassaballah. 2020. Hybrid Harris Hawks optimization with cuckoo search for drug design and discovery in chemoinformatics. Scientific Reports 10 (1):1–22. doi:10.1038/s41598-020-71502-z.
   PubMed Web of Science ®Google Scholar
• Humada, A. M., M. Hojabri, S. Mekhilef, and H. M. Hamada. 2016. Solar cell parameters extraction based on single and double-diode models: A review. Renewable and Sustainable Energy Reviews 56:494–509. doi:10.1016/j.rser.2015.11.051.
   Web of Science ®Google Scholar
• Iqbal, J., A. A. Zahrani, S. A. Alharbi, and A. Hashmi. 2019. Robotics inspired renewable energy developments: prospective opportunities and challenges. Institute of Electrical and Electronics EngineersAccess 7:174898–923. doi:10.1109/ACCESS.2019.2957013.
   Google Scholar
• Javidy, B., A. Hatamlou, and S. Mirjalili. 2015. Ions motion algorithm for solving optimization problems. Applied Soft Computing 32:72–79. doi:10.1016/j.asoc.2015.03.035.
   Web of Science ®Google Scholar
• Jiao, S., G. Chong, C. Huang, H. Hu, M. Wang, A. A. Heidari, H. Chen, and X. Zhao. 2020. Orthogonally adapted Harris Hawks optimization for parameter estimation of photovoltaic models. Energy 203:117804. doi:10.1016/j.energy.2020.117804.
   Web of Science ®Google Scholar
• Jordehi, A. R. 2016. Parameter estimation of solar photovoltaic (PV) cells: A review. Renewable and Sustainable Energy Reviews 61:354–71. doi:10.1016/j.rser.2016.03.049.
   Web of Science ®Google Scholar
• Jordehi, A. R. 2018. Enhanced leader particle swarm optimisation (ELPSO): an efficient algorithm for parameter estimation of photovoltaic (PV) cells and modules. Solar Energy 159:78–87. doi:10.1016/j.solener.2017.10.063.
   Web of Science ®Google Scholar
• Ju, F. Y., and W. C. Hong. 2013. Application of seasonal SVR with chaotic gravitational search algorithm in electricity forecasting. Applied Mathematical Modelling 37 (23):9643–51. doi:10.1016/j.apm.2013.05.016.
   Web of Science ®Google Scholar
• Kamel, S., E. H. Houssein, M. H. Hassan, M. Shouran, and F. A. Hashim. 2022. An efficient electric charged particles optimization algorithm for numerical optimization and optimal estimation of photovoltaic models. Mathematics 10 (6):913. doi:10.3390/math10060913.
   Web of Science ®Google Scholar
• Kezhong, L., and M. Zongmin. 2021. A modified whale optimization algorithm for parameter estimation of software reliability growth models. Journal of Algorithms & Computational Technology 15:174–88. doi:10.1177/17483026211034442.
   Web of Science ®Google Scholar
• Konneh, K. V., H. Masrur, M. L. Othman, N. I. A. Wahab, H. Hizam, S. Z. Islam, P. Crossley, T. Senjyu . 2021. Optimal design and performance analysis of a hybrid off-grid renewable power system considering different component scheduling, PV modules, and solar tracking systems. Institute of Electrical and Electronics EngineersAccess 9:64393–413. doi:10.1109/ACCESS.2021.3075732.
   Google Scholar
• Kumar, C., T. D. Raj, M. Premkumar, and T. D. Raj. 2020. A new stochastic slime mould optimization algorithm for the estimation of solar photovoltaic cell parameters. Optik 223:165277. doi:10.1016/j.ijleo.2020.165277.
   Web of Science ®Google Scholar
• Kumar, S., A. Singh, and A. Dhar. 2017. Parameter extraction using global particle swarm optimization approach and the influence of polymer processing temperature on the solar cell parameters. AIP Advances 7 (8):85117. doi:10.1063/1.4993999.
   Web of Science ®Google Scholar
• Lee, K. Y., and J. B. Park. 2006. Application of particle swarm optimization to economic dispatch problem: advantages and disadvantages. In 2006 IEEE PES Power Systems Conference and Exposition, Atlanta, GA, USA. 188–92.
   Google Scholar
• Li, Y., and Q. Yang. 2019. Optimal storage sizing of energy storage for peak shaving in presence of uncertainties in distributed energy management systems. International Journal of Modelling, Identification and Control 31 (1):72–80. doi:10.1504/IJMIC.2019.096840.
   Web of Science ®Google Scholar
• Mirjalili, S., and A. H. Gandomi. 2017. Chaotic gravitational constants for the gravitational search algorithm. Applied Soft Computing 53:407–19. doi:10.1016/j.asoc.2017.01.008.
   Web of Science ®Google Scholar
• Nasir, M., H. A. Khan, A. Hussain, L. Mateen, and N. A. Zaffar. 2017. Solar PV-based scalable DC microgrid for rural electrification in developing regions. IEEE Transactions on Sustainable Energy 9 (1):390–99. doi:10.1109/TSTE.2017.2736160.
   Web of Science ®Google Scholar
• Ortiz-Conde, A., D. Lugo-Munoz, and F. J. Garcia-Sánchez. 2012. An explicit multiexponential model as an alternative to traditional solar cell models with series and shunt resistances. IEEE Journal of Photovoltaics 2 (3):261–68. doi:10.1109/JPHOTOV.2012.2190265.
   Web of Science ®Google Scholar
• Pattnayak, S. K., S. Choudhury, N. Nayak, D. P. Bagarty, and M. Biswabandhya. 2020. Maximum power tracking and harmonic reduction on grid PV system using chaotic gravitational search algorithm based MPPT controller. In 2020 International Conference on Computational Intelligence for Smart Power System and Sustainable Energy (CISPSSE), Keonjhar, India. 1–6.
   Google Scholar
• Premkumar, M., P. Jangir, R. M. Elavarasan, and R. Sowmya. 2021. Opposition decided gradient-based optimizer with balance analysis and diversity maintenance for parameter identification of solar photovoltaic models. Journal of Ambient Intelligence and Humanized Computing 1–23. doi:10.1007/s12652-021-03564-4.
   Web of Science ®Google Scholar
• Premkumar, M., P. Jangir, C. Ramakrishnan, G. Nalinipriya, H. H. Alhelou, and B. S. Kumar. 2021. Identification of solar photovoltaic model parameters using an improved gradient-based optimization algorithm with chaotic drifts. Institute of Electrical and Electronics EngineersAccess 9:62347–79. doi:10.1109/ACCESS.2021.3073821.
   Google Scholar
• Qais, M. H., and S. M. Muyeen. 2021. A novel adaptive filtering algorithm based parameter estimation technique for photovoltaic system. IEEE Transactions on Energy Conversion 37 (1):286–94. doi:10.1109/TEC.2021.3090943.
   Web of Science ®Google Scholar
• Raheed, M., S. Shihab, and T. Rashid. 2021. Two step and newton-raphson algorithms in the extraction for the parameters of solar cell. Al-Qadisiyah Journal of Pure Science 26 (1):143–54. doi:10.29350/qjps.2021.26.1.1260.
   Google Scholar
• Rashedi, E., H. Nezamabadi-Pour, and S. Saryazdi. 2009. GSA: A gravitational search algorithm. Information Sciences 179 (13):2232–48. doi:10.1016/j.ins.2009.03.004.
   Web of Science ®Google Scholar
• Rather, S. A., and P. S. Bala. 2020. Swarm-based chaotic gravitational search algorithm for solving mechanical engineering design problems. World Journal of Engineering 17 (1):97–114. doi:10.1108/WJE-09-2019-0254.
   Web of Science ®Google Scholar
• Ridha, H. M. 2020. Parameters extraction of single and double diodes photovoltaic models using marine predators algorithm and Lambert W function. Solar Energy 209:674–93. doi:10.1016/j.solener.2020.09.047.
   Web of Science ®Google Scholar
• Shankar, N., N. Saravanakumar, C. Kumar, V. K. Kannan, and B. I. Rani. 2021. Opposition-based equilibrium optimizer algorithm for identification of equivalent circuit parameters of various photovoltaic models. Journal of Computational Electronics 20 (4):1560–87. doi:10.1007/s10825-021-01722-7.
   Web of Science ®Google Scholar
• Singh, O., A. Ghosh, A. K. Ray, and A. K. Yadav. 2021. An arduino based on-site monitoring and gravitational search algorithm based parameter estimation for PV module. Transactions of the Indian National Academy of Engineering 6 (3):805–18. doi:10.1007/s41403-021-00246-7.
   Google Scholar
• Vandrasi, R. K., B. S. Kumar, and R. Devarapalli. 2022. Solar photovoltaic module parameter extraction using a novel hybrid chimp-sine cosine algorithm. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 2:1–20. doi:10.1080/15567036.2022.2041768.
   Google Scholar
• Vithayasrichareon, P., G. Mills, and I. F. MacGill. 2015. Impact of electric vehicles and solar PV on future generation portfolio investment. IEEE Transactions on Sustainable Energy 6 (3):899–908. doi:10.1109/TSTE.2015.2418338.
   Web of Science ®Google Scholar
• Wolpert, D. H., and W. G. Macready. 1997. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1 (1):67–82. doi:10.1109/4235.585893.
   Google Scholar
• Xiong, G., J. Zhang, X. Yuan, D. Shi, Y. He, and G. Yao. 2018. Parameter extraction of solar photovoltaic models by means of a hybrid differential evolution with whale optimization algorithm. Solar Energy 176:742–61. doi:10.1016/j.solener.2018.10.050.
   Web of Science ®Google Scholar
• Yu, K., B. Qu, C. Yue, S. Ge, X. Chen, and J. Liang. 2019. A performance-guided JAYA algorithm for parameters identification of photovoltaic cell and module. Applied Energy 237:241–57. doi:10.1016/j.apenergy.2019.01.008.
   Web of Science ®Google Scholar