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Parameter estimation of three-diode solar photovoltaic model using an Improved-African Vultures optimization algorithm with Newton–Raphson method

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Abstract

Parameter identification and accurate photovoltaic (PV) modeling from basic I–V information are necessary for simulation, optimization, and control of the PV systems. Therefore, this paper proposes an Improved-African Vultures Optimization (I-AVO) algorithm, which combines the general Opposition-Based Learning (OBL) and Orthogonal Learning to extract the unknown parameters of the solar Photovoltaic (PV) modules accurately and effectually. The proposed I-AVO algorithm is developed from the basic version of the recently proposed African Vultures Optimization (AVO) algorithm. The solar PV parameters estimation problem is considered to be a complex optimization problem with the characteristics such as multi-dimensional, nonlinear, Transcendental, and multi-modal. Therefore, the basic variant of AVO struggles to produce the optimal and is stuck at local optima when it handles this complex optimization problem. Therefore, the I-AVO is formulated by combining the features of OL and OBL, along with the AVO, to generate the optimal solution. Out of various PV models, Three-Diode Model has been considered to determine the parameters. Furthermore, Newton–Raphson (NR) technique is discussed to solve the chaotic behavior of the I–V curve relation. The obtained results proved that the proposed I-AVO along with NR, called I-AVO-NR, can accurately obtain the optimal solution. The superiority of the proposed algorithm is proved to be better than other advanced algorithms based on the obtained results and their comparison. Based on the statistical test value obtained from Friedman’s test, the proposed algorithm stood first among eight algorithms with the ranking value of 1.542 for two case studies.

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References

  1. Menaga, D., Premkumar, M., Sowmya, R., Narasimman, S.: Design of nonlinear uncertainty controller for grid-tied solar photovoltaic system using sliding mode control. Energy Eng. J. Assoc. Energy Eng. 117, 481 (2020). https://doi.org/10.32604/EE.2020.013282

    Article  Google Scholar 

  2. Ola, S.R., Saraswat, A., Goyal, S.K., Jhajharia, S.K., Mahela, O.P.: Detection and analysis of power system faults in the presence of wind power generation using stockwell transform based median. (2020)

  3. Premkumar, M., Sumithira, T.R.: Design and implementation of new topology for solar PV based transformerless forward microinverter. J. Elect. Eng. Technol. 14, 1–11 (2019). https://doi.org/10.1007/s42835-018-00036-2

    Article  Google Scholar 

  4. Premkumar, M., Sowmya, R., Karthick, K.: A dataset of the study on design parameters for the solar photovoltaic charge controller. Data Brief 21, 1954–1962 (2018). https://doi.org/10.1016/j.dib.2018.11.064

    Article  Google Scholar 

  5. Silva, I. da, Ronoh, G., Maranga, I., Odhiambo, M., Kiyegga, R.: Implementing the SDG 2, 6 and 7 Nexus in Kenya—a case study of solar powered water pumping for human consumption and irrigation. World Sustain. Ser. 933–942 (2020). https://doi.org/10.1007/978-3-030-26759-9_55

  6. Pace, L.A.: How do tourism firms innovate for sustainable energy consumption? A capabilities perspective on the adoption of energy efficiency in tourism accommodation establishments. J. Clean. Prod. 111, 409–420 (2016). https://doi.org/10.1016/J.JCLEPRO.2015.01.095

    Article  Google Scholar 

  7. Jung, D., Salmon, A., Gese, P.: Agrivoltaics for farmers with shadow and electricity demand: results of a pre-feasibility study under net billing in central Chile. In: AIP conference proceedings, p. 030001. AIP Publishing LLC AIP Publishing (2021)

  8. Lazzeroni, P., Olivero, S., Repetto, M., Stirano, F., Vallet, M.: Optimal battery management for vehicle-to-home and vehicle-to-grid operations in a residential case study. Energy 175, 704–721 (2019). https://doi.org/10.1016/J.ENERGY.2019.03.113

    Article  Google Scholar 

  9. Mehrjerdi, H., Bornapour, M., Hemmati, R., Ghiasi, S.M.S.: Unified energy management and load control in building equipped with wind-solar-battery incorporating electric and hydrogen vehicles under both connected to the grid and islanding modes. Energy 168, 919–930 (2019). https://doi.org/10.1016/J.ENERGY.2018.11.131

    Article  Google Scholar 

  10. Mehrjerdi, H., Rakhshani, E.: Vehicle-to-grid technology for cost reduction and uncertainty management integrated with solar power. J. Clean. Prod. 229, 463–469 (2019). https://doi.org/10.1016/J.JCLEPRO.2019.05.023

    Article  Google Scholar 

  11. El-Faouri, F.S., Sharaiha, M., Bargouth, D., Faza, A.: A smart street lighting system using solar energy. IEEE PES Innov. Smart Grid Technol. Conf. Europe. (2016). https://doi.org/10.1109/ISGTEUROPE.2016.7856255

    Article  Google Scholar 

  12. Sutopo, W., Mardikaningsih, I.S., Zakaria, R., Ali, A.: A model to improve the implementation standards of street lighting based on solar energy: a case study. Energies 13, 630 (2020). https://doi.org/10.3390/EN13030630

  13. Eidan, A.A., AlSahlani, A., Ahmed, A.Q., Al-fahham, M., Jalil, J.M.: Improving the performance of heat pipe-evacuated tube solar collector experimentally by using Al2O3 and CuO/acetone nanofluids. Sol. Energy 173, 780–788 (2018). https://doi.org/10.1016/J.SOLENER.2018.08.013

    Article  Google Scholar 

  14. Wang, T., Zhao, Y., Diao, Y., Ren, R., Wang, Z.: Performance of a new type of solar air collector with transparent-vacuum glass tube based on micro-heat pipe arrays. Energy 177, 16–28 (2019). https://doi.org/10.1016/J.ENERGY.2019.04.059

  15. Sarkar, S., Bhaskar, M.S., Uma Rao, K., V, P., Almakhles, D., Subramaniam, U.: Solar PV network installation standards and cost estimation guidelines for smart cities. Alexandria Eng. J. (2021). https://doi.org/10.1016/J.AEJ.2021.06.098

  16. Malinowski, M., Leon, J.I., Abu-Rub, H.: Solar photovoltaic and thermal energy systems: current technology and future trends. Proc. IEEE 105, 2132–2146 (2017). https://doi.org/10.1109/JPROC.2017.2690343

    Article  Google Scholar 

  17. Fthenakis, V.M., Hyung, C.K., Alsema, E.: Emissions from photovoltaic life cycles. Environ. Sci. Technol. 42, 2168–2174 (2008). https://doi.org/10.1021/ES071763Q

    Article  Google Scholar 

  18. Solar Energy Isn’t Always as Green as You Think—IEEE Spectrum, https://spectrum.ieee.org/solar-energy-isnt-always-as-green-as-you-think#toggle-gdpr

  19. Almosni, S., Delamarre, A., Jehl, Z., Suchet, D., Cojocaru, L., Giteau, M., Behaghel, B., Julian, A., Ibrahim, C., Tatry, L., Wang, H., Kubo, T., Uchida, S., Segawa, H., Miyashita, N., Tamaki, R., Shoji, Y., Yoshida, K., Ahsan, N., Watanabe, K., Inoue, T., Sugiyama, M., Nakano, Y., Hamamura, T., Toupance, T., Olivier, C., Chambon, S., Vignau, L., Geffroy, C., Cloutet, E., Hadziioannou, G., Cavassilas, N., Rale, P., Cattoni, A., Collin, S., Gibelli, F., Paire, M., Lombez, L., Aureau, D., Bouttemy, M., Etcheberry, A., Okada, Y., Guillemoles, J.-F.: Material challenges for solar cells in the twenty-first century: directions in emerging technologies. Sci. Technol. Adv. Mater. 19, 336–369 (2018). https://doi.org/10.1080/14686996.2018.1433439

    Article  Google Scholar 

  20. Bube, R.H.: Photovolt. Mater. (1998). https://doi.org/10.1142/P054

    Article  Google Scholar 

  21. Schropp, R., Franken, R., Gordijn, A., Zambrano, R.J., Li, H., Löffler, J., Rath, J., Stolk, R., van Veen, M., van der Werf, K.: Thin film silicon alloys with enhanced stability made by PECVD and HWCVD for multibandgap solar cells. Conference Record of the IEEE Photovoltaic Specialists Conference. 1371–1376 (2005). https://doi.org/10.1109/PVSC.2005.1488396

  22. Schropp, R.E.I., Zeman, M.: New developments in amorphous thin-film silicon solar cells. IEEE Trans. Electron Devices 46, 2086–2092 (1999). https://doi.org/10.1109/16.792001

    Article  Google Scholar 

  23. Premkumar, M., Sowmya, R.: An effective maximum power point tracker for partially shaded solar photovoltaic systems. Energy Rep. 5, 1445–1462 (2019). https://doi.org/10.1016/j.egyr.2019.10.006

    Article  Google Scholar 

  24. Ayang, A., Wamkeue, R., Ouhrouche, M., Djongyang, N., Essiane Salomé, N., Pombe, J.K., Ekemb, G.: Maximum likelihood parameters estimation of single-diode model of photovoltaic generator. Renewab. Energy. 130, (2019). https://doi.org/10.1016/j.renene.2018.06.039

  25. Mohamed, N., Alrahim, A., Yahaya, N.Z., Singh, B.: Single-diode model and two-diode model of PV modules : a comparison. In: 2013 IEEE International Conference on Control System, Computing and Engineering, pp. 210–214 (2013)

  26. Rasheed, M.S., Shihab, S.: Modelling and parameter extraction of PV cell using single-diode model. Adv. Energy Conv. Mater. (2020). https://doi.org/10.37256/aecm.122020550

  27. Humada, A.M., Hojabri, M., Mekhilef, S., Hamada, H.M.: Solar cell parameters extraction based on single and double-diode models: a review. Renew. Sustain. Energy Rev. 56, 494–509 (2016). https://doi.org/10.1016/j.rser.2015.11.051

    Article  Google Scholar 

  28. Sandrolini, L., Artioli, M., Reggiani, U.: Numerical method for the extraction of photovoltaic module double-diode model parameters through cluster analysis. Appl. Energy 87, 442–451 (2010). https://doi.org/10.1016/j.apenergy.2009.07.022

    Article  Google Scholar 

  29. Chen, Y., Sun, Y., Meng, Z.: An improved explicit double-diode model of solar cells: fitness verification and parameter extraction. Energy Conv. Manage. 169, (2018). https://doi.org/10.1016/j.enconman.2018.05.035

  30. Lun, S., Wang, S., Yang, G., Guo, T.: A new explicit double-diode modeling method based on Lambert W-function for photovoltaic arrays. Solar Energy 116, (2015). https://doi.org/10.1016/j.solener.2015.03.043

  31. Qais, M.H., Hasanien, H.M., Alghuwainem, S.: Parameters extraction of three-diode photovoltaic model using computation and Harris Hawks optimization. Energy. 195, 117040 (2020). https://doi.org/10.1016/j.energy.2020.117040

  32. Qais, M.H., Hasanien, H.M., Alghuwainem, S.: Parameters extraction of three-diode photovoltaic model using computation and Harris Hawks optimization. Energy 195, 117040 (2020). https://doi.org/10.1016/j.energy.2020.117040

    Article  Google Scholar 

  33. Qais, M.H., Hasanien, H.M., Alghuwainem, S.: Identification of electrical parameters for three-diode photovoltaic model using analytical and sunflower optimization algorithm. Appl. Energy 250, 109–117 (2019). https://doi.org/10.1016/j.apenergy.2019.05.013

  34. Yousri, D., Thanikanti, S.B., Allam, D., Ramachandaramurthy, V.K., Eteiba, M.B.: Fractional chaotic ensemble particle swarm optimizer for identifying the single, double, and three diode photovoltaic models’ parameters. Energy 195, 116979 (2020). https://doi.org/10.1016/j.energy.2020.116979

  35. Khanna, V., Das, B.K., Bisht, D., Vandana, Singh, P.K.: A three diode model for industrial solar cells and estimation of solar cell parameters using PSO algorithm. Renewab. Energy 78, 105–113 (2015). https://doi.org/10.1016/j.renene.2014.12.072

  36. Premkumar, M., Sowmya, R.: Certain study on MPPT algorithms to track the global MPP under partial shading on solar PV module/array. Int. J. Comput. Dig. Syst. 8, 405–416 (2019). https://doi.org/10.12785/ijcds/080409

  37. Premkumar, M., Sumithira, R.: Humpback whale assisted hybrid maximum power point tracking algorithm for partially shaded solar photovoltaic systems. J. Power Electron. 18, 1805–1818 (2018). https://doi.org/10.6113/JPE.2018.18.6.1805

    Article  Google Scholar 

  38. Premkumar, M., Kumar, C., Sowmya, R., Pradeep, J.: A novel salp swarm assisted hybrid maximum power point tracking algorithm for the solar photovoltaic power generation systems. Automatika 62, (2021). https://doi.org/10.1080/00051144.2020.1834062

  39. Haque, A., Bharath, K.V.S., Khan, M.A., Khan, I., Jaffery, Z.A.: Fault diagnosis of photovoltaic modules. Energy Sci. Eng. 7, 622–644 (2019). https://doi.org/10.1002/ESE3.255

    Article  Google Scholar 

  40. Padmavathi, N., Chilambuchelvan, A.: Fault detection and identification of solar panels using Bluetooth. In: 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017, pp. 3420–3426 (2018). https://doi.org/10.1109/ICECDS.2017.8390096

  41. Hu, H., Harb, S., Kutkut, N., Batarseh, I., Shen, Z.J.: A review of power decoupling techniques for microinverters with three different decoupling capacitor locations in PV systems. IEEE Trans. Power Electron. 28, (2013). https://doi.org/10.1109/TPEL.2012.2221482

  42. Nunes, H.G.G., Pombo, J.A.N., Bento, P.M.R., Mariano, S.J.P.S., Calado, M.R.A.: Collaborative swarm intelligence to estimate PV parameters. Energy Convers. Manage. 185, 866–890 (2019). https://doi.org/10.1016/j.enconman.2019.02.003

    Article  Google Scholar 

  43. Jadli, U., Thakur, P., Shukla, R.D.: A new parameter estimation method of solar photovoltaic. IEEE J. Photovolt. 8 (2018). https://doi.org/10.1109/JPHOTOV.2017.2767602

  44. Babu, B.C., Gurjar, S.: A novel simplified two-diode model of photovoltaic (PV) module. IEEE J. Photovolt. 4, 1156–1161 (2014). https://doi.org/10.1109/JPHOTOV.2014.2316371

    Article  Google Scholar 

  45. Reis, L.R.D., Camacho, J.R., Novacki, D.F.: The newton raphson method in the extraction of parameters of PV modules. Renewab. Energy Power Qual. J. 1, (2017). https://doi.org/10.24084/repqj15.416

  46. Ridha, H.M., Hizam, H., Gomes, C., Heidari, A.A., Chen, H., Ahmadipour, M., Muhsen, D.H., Alghrairi, M.: Parameters extraction of three diode photovoltaic models using boosted LSHADE algorithm and Newton Raphson method. Energy 224, 120136 (2021). https://doi.org/10.1016/j.energy.2021.120136

  47. Kalantari, B.: Generalization of Taylor’s theorem and Newton’s method via a new family of determinantal interpolation formulas and its applications. J. Comput. Appl. Math. 126, (2000). https://doi.org/10.1016/S0377-0427(99)00360-X

  48. Kanimozhi, G.: Harish Kumar: modeling of solar cell under different conditions by Ant Lion Optimizer with LambertW function. Appl. Comput. J. 71, 141–151 (2018). https://doi.org/10.1016/j.asoc.2018.06.025

    Article  Google Scholar 

  49. El-Fergany, A.A.: Parameters identification of PV model using improved slime mould optimizer and Lambert W-function. Energy Rep. 7, 875–887 (2021). https://doi.org/10.1016/j.egyr.2021.01.093

  50. Ridha, H.M.: Parameters extraction of single and double diodes photovoltaic models using Marine Predators Algorithm and Lambert W function. Solar Energy 209, 674–693 (2020). https://doi.org/10.1016/j.solener.2020.09.047

  51. Wong, W.K., Ming, C.I.: A review on metaheuristic algorithms: recent trends, benchmarking and applications. In: 2019 7th International Conference on Smart Computing and Communications, pp. 1–5. ICSCC 2019 (2019). https://doi.org/10.1109/ICSCC.2019.8843624

  52. Gunantara, N.: A review of multi-objective optimization: methods and its applications. Cogent Eng. 5, 1–16 (2018). https://doi.org/10.1080/23311916.2018.1502242

    Article  Google Scholar 

  53. Yohanandhan, R.V., Elavarasan, R.M., Premkumar, M., Mihet-Popa, L.: Cyber-physical power system (CPPS): a review on modelling, simulation, and analysis with cyber security applications. IEEE Access. 1 (2020). https://doi.org/10.1109/access.2020.3016826

  54. Ramadan, A., Kamel, S., Korashy, A., Yu, J.: Photovoltaic cells parameter estimation using an enhanced teaching–learning-based optimization algorithm. Iranian Journal of Sci. Technol. Trans. Elect. Eng. 44 (2020). https://doi.org/10.1007/s40998-019-00257-9

  55. Kiani, A.T., Nadeem, M.F., Ahmed, A., Sajjad, I.A., Haris, M.S., Martirano, L.: Optimal parameter estimation of solar cell using simulated annealing inertia weight particle swarm optimization (SAIW-PSO). In: Proceedings—2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2020 (2020)

  56. Abido, M.A., Khalid, M.S.: Seven-parameter PV model estimation using differential evolution. Electr. Eng. 100, 971–981 (2018). https://doi.org/10.1007/s00202-017-0542-2

    Article  Google Scholar 

  57. Montoya, O.D., Gil-González, W., Grisales-Noreña, L.F.: Sine-cosine algorithm for parameters’ estimation in solar cells using datasheet information. J. Phys: Conf. Ser. 1671, 012008 (2020). https://doi.org/10.1088/1742-6596/1671/1/012008

    Article  Google Scholar 

  58. Saxena, A., Sharma, A., Shekhawat, S.: Parameter extraction of solar cell using intelligent grey wolf optimizer. Evol. Intel. (2020). https://doi.org/10.1007/s12065-020-00499-1

    Article  Google Scholar 

  59. Premkumar, M., Sowmya, R., Umashankar, S., Jangir, P.: Extraction of uncertain parameters of single-diode photovoltaic module using hybrid particle swarm optimization and grey wolf optimization algorithm. Mater. Today Proc. (2020). https://doi.org/10.1016/j.matpr.2020.08.784

    Article  Google Scholar 

  60. Dhiman, G., Kumar, V.: Seagull optimization algorithm: theory and its applications for large-scale industrial engineering problems. Knowl. Based Syst. 165, 169–196 (2019). https://doi.org/10.1016/j.knosys.2018.11.024

    Article  Google Scholar 

  61. Premkumar, M., Sowmya, R., Jangir, P., Siva Kumar, J.S.V.: A new and reliable objective functions for extracting the unknown parameters of solar photovoltaic cell using political optimizer algorithm. In: 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020 (2020)

  62. Premkumar, M., Babu, T.S., Umashankar, S., Sowmya, R.: A new metaphor-less algorithms for the photovoltaic cell parameter estimation. Optik 208, 164559 (2020). https://doi.org/10.1016/j.ijleo.2020.164559

  63. Oliva, D., Abd El Aziz, M., Ella Hassanien, A.: Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm. Appl. Energy. 200, 141–154 (2017). https://doi.org/10.1016/j.apenergy.2017.05.029

  64. Premkumar, M., Jangir, P., Ramakrishnan, C., Nalinipriya, G., Alhelou, H.H., Kumar, B.S.: Identification of solar photovoltaic model parameters using an improved gradient-based optimization algorithm with chaotic drifts. IEEE Access. 9, 62347–62379 (2021). https://doi.org/10.1109/ACCESS.2021.3073821

    Article  Google Scholar 

  65. Abdel-basset, M., Mohamed, R., Mirjalili, S., Chakrabortty, R.K., Ryan, M.J.: Solar photovoltaic parameter estimation using an improved equilibrium optimizer. Sol. Energy 209, 694–708 (2020). https://doi.org/10.1016/j.solener.2020.09.032

    Article  Google Scholar 

  66. Mousa, A.A., El-Shorbagy, M.A., Mustafa, I., Alotaibi, H.: Chaotic search based equilibrium optimizer for dealing with nonlinear programming and petrochemical application. Processes. 9, (2021). https://doi.org/10.3390/pr9020200

  67. Sheng, H., Li, C., Wang, H., Yan, Z., Xiong, Y., Cao, Z., Kuang, Q.: Parameters extraction of photovoltaic models using an improved moth-flame optimization. Energies 12, 3527 (2019). https://doi.org/10.3390/en12183527

    Article  Google Scholar 

  68. Kumar, C., Raj, T.D., Premkumar, M., Raj, T.D.: A new stochastic slime mould optimization algorithm for the estimation of solar photovoltaic cell parameters. Optik 223, 165277 (2020). https://doi.org/10.1016/j.ijleo.2020.165277

    Article  Google Scholar 

  69. Soliman, M.A., Hasanien, H.M.: Marine predators algorithm for parameters identification of triple-diode photovoltaic models. IEEE Access. 8, 155832 (2020). https://doi.org/10.1109/ACCESS.2020.3019244

    Article  Google Scholar 

  70. Jangir, P., Buch, H., Mirjalili, S., Manoharan, P.: MOMPA: Multi-objective marine predator algorithm for solving multi-objective optimization problems. Evol. Intel. 2021, 1–27 (2021). https://doi.org/10.1007/S12065-021-00649-Z

    Article  Google Scholar 

  71. Yang, Y., Chen, H., Asghar Heidari, A., Gandomi, A.H.: Hunger games search: visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Exp. Syst. Appl. 114864 (2021). https://doi.org/10.1016/j.eswa.2021.114864

  72. Manjula Devi, R., Premkumar, M., Jangir, P., Santhosh Kumar, B., Alrowaili, D., Sooppy Nisar, K.: BHGSO: Binary hunger games search optimization algorithm for feature selection problem. Comput. Mater. Continua. 70, 557–579 (2022). https://doi.org/10.32604/CMC.2022.019611

  73. Ahmadianfar, I., Heidari, A.A., Gandomi, A.H., Chu, X., Chen, H.: RUN beyond the metaphor: an efficient optimization algorithm based on Runge Kutta method. Exp. Syst. Appl. 181, 115079 (2021). https://doi.org/10.1016/j.eswa.2021.115079

    Article  Google Scholar 

  74. Jordehi, A.R.: Parameter estimation of solar photovoltaic (PV) cells : A review. Renew. Sustain. Energy Rev. 61, 354–371 (2016). https://doi.org/10.1016/j.rser.2016.03.049

    Article  Google Scholar 

  75. Chin, V.J., Salam, Z., Ishaque, K.: Cell modelling and model parameters estimation techniques for photovoltaic simulator application: A review. Appl. Energy 154, 500–519 (2015). https://doi.org/10.1016/j.apenergy.2015.05.035

    Article  Google Scholar 

  76. Ahmadianfar, I., Gong, W., Heidari, A.A., Golilarz, N.A., Samadi-Koucheksaraee, A., Chen, H.: Gradient-based optimization with ranking mechanisms for parameter identification of photovoltaic systems. Energy Rep. 7, 3979–3997 (2021)

    Article  Google Scholar 

  77. Hu, Z., Gong, W., Li, S.: Reinforcement learning-based differential evolution for parameters extraction of photovoltaic models. Energy Rep. 7, 916–928 (2021)

    Article  Google Scholar 

  78. Muhsen, D.H., Ghazali, A.B., Khatib, T., Abed, I.A.: A comparative study of evolutionary algorithms and adapting control parameters for estimating the parameters of a single-diode photovoltaic module’s model. Renew. Energy 96, 377–389 (2016). https://doi.org/10.1016/J.RENENE.2016.04.072

    Article  Google Scholar 

  79. Premkumar, M., Jangir, P., Sowmya, R., Elavarasan, R.M., Kumar, B.S.: Enhanced chaotic JAYA algorithm for parameter estimation of photovoltaic cell/modules. ISA Trans. (2021). https://doi.org/10.1016/j.isatra.2021.01.045

    Article  Google Scholar 

  80. Yu, K., Liang, J.J., Qu, B.Y., Chen, X., Wang, H.: Parameters identification of photovoltaic models using an improved JAYA optimization algorithm. Energy Convers. Manage. 150, 742–753 (2017). https://doi.org/10.1016/j.enconman.2017.08.063

    Article  Google Scholar 

  81. Shaheen, M.A.M., Hasanien, H.M., Alkuhayli, A.: A novel hybrid GWO-PSO optimization technique for optimal reactive power dispatch problem solution. Ain Shams Eng. J. (2020). https://doi.org/10.1016/j.asej.2020.07.011

    Article  Google Scholar 

  82. Long, W., Cai, S., Jiao, J., Xu, M., Wu, T.: A new hybrid algorithm based on grey wolf optimizer and cuckoo search for parameter extraction of solar photovoltaic models. Energy Convers. Manage. 203, 112243 (2020). https://doi.org/10.1016/j.enconman.2019.112243

  83. Fan, Q., Huang, H., Yang, K., Zhang, S., Yao, L., Xiong, Q.: A modified equilibrium optimizer using opposition-based learning and novel update rules. Exp. Syst. Appl. 170, 114575 (2021). https://doi.org/10.1016/j.eswa.2021.114575

  84. Dai, C., Hu, Z., Li, Z., Xiong, Z., Su, Q.: An improved grey prediction evolution algorithm based on topological opposition-based learning. IEEE Access. 8, (2020). https://doi.org/10.1109/ACCESS.2020.2973197

  85. Oliva, D., Elaziz, M.A., Elsheikh, A.H., Ewees, A.A.: A review on meta-heuristics methods for estimating parameters of solar cells. J. Power Sour. 435, 126683 (2019). https://doi.org/10.1016/J.JPOWSOUR.2019.05.089

    Article  Google Scholar 

  86. el Tayyan, A.A.: An approach to extract the parameters of solar cells from their illuminated I—V curves using the Lambert W function. Turkish J. Phys. 39, (2015). https://doi.org/10.3906/fiz-1309-7

  87. Biswas, P.P., Suganthan, P.N., Wu, G., Amaratunga, G.A.J.: Parameter estimation of solar cells using datasheet information with the application of an adaptive differential evolution algorithm. Renew. Energy 132, 425–438 (2019). https://doi.org/10.1016/j.renene.2018.07.152

    Article  Google Scholar 

  88. Tripathy, M., Kumar, M., Sadhu, P.K.: Photovoltaic system using Lambert W function-based technique. Solar Energy. 158 (2017). https://doi.org/10.1016/j.solener.2017.10.007

  89. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1, 67–82 (1997)

    Article  Google Scholar 

  90. Abdollahzadeh, B., Gharehchopogh, F.S., Mirjalili, S.: African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Comput. Ind. Eng. 158, 107408 (2021). https://doi.org/10.1016/J.CIE.2021.107408

    Article  Google Scholar 

  91. Tizhoosh, H.R.: Opposition-based learning: a new scheme for machine intelligence. In: Proceedings—International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet vol. 1, pp. 695–701 (2005). https://doi.org/10.1109/cimca.2005.1631345

  92. Jiao, S., Chong, G., Huang, C., Hu, H., Wang, M., Asghar, A., Chen, H., Zhao, X.: Orthogonally adapted Harris hawks optimization for parameter estimation of photovoltaic models. Energy 203, 117804 (2020). https://doi.org/10.1016/j.energy.2020.117804

    Article  Google Scholar 

  93. Zhang, H., Heidari, A.A., Wang, M., Zhang, L., Chen, H., Li, C.: Orthogonal Nelder-Mead moth flame method for parameters identification of photovoltaic modules. Energy Convers. Manage. 211 (2020). https://doi.org/10.1016/j.enconman.2020.112764

  94. Wei, T., Yu, F., Huang, G., Xu, C.: A particle-swarm-optimization-based parameter extraction routine for three-diode lumped parameter model of organic solar cells. IEEE Electron Dev. Lett. 40, 1511–1514 (2019). https://doi.org/10.1109/LED.2019.2926315

    Article  Google Scholar 

  95. Premkumar, M., Kumar, C., Anbarasan, A., Sowmya, R.: A new maximum power point tracking technique based on whale optimisation algorithm for solar photovoltaic systems, pp. 1–11 (2021). https://doi.org/10.1080/01430750.2021.1969270

  96. Xiao, W.B., Liu, W.Q., Wu, H.M., Zhang, H.M.: Review of parameter extraction methods for single-diode model of solar cell. Wuli Xuebao/Acta Physica Sinica. 67 (2018). https://doi.org/10.7498/aps.67.20181024

  97. Xavier, F.J., Pradeep, A., Premkumar, M., Kumar, C.: Orthogonal learning-based Gray Wolf Optimizer for identifying the uncertain parameters of various photovoltaic models. Optik 247, 167973 (2021). https://doi.org/10.1016/J.IJLEO.2021.167973

    Article  Google Scholar 

  98. Qais, M.H., Hasanien, H.M., Alghuwainem, S., Nouh, A.S.: Coyote optimization algorithm for parameters extraction of three-diode photovoltaic models of photovoltaic modules. Energy 187, 116001 (2019). https://doi.org/10.1016/j.energy.2019.116001

    Article  Google Scholar 

  99. Zhao, X., Fang, Y., Liu, L., Li, J., Xu, M.: An improved moth-flame optimization algorithm with orthogonal opposition-based learning and modified position updating mechanism of moths for global optimization problems. Appl. Intell. 50 (2020). https://doi.org/10.1007/s10489-020-01793-2

  100. Zhang, H., Li, R., Cai, Z., Gu, Z., Heidari, A.A., Wang, M., Chen, H., Chen, M.: Advanced orthogonal moth flame optimization with Broyden–Fletcher–Goldfarb–Shanno algorithm: framework and real-world problems. Exp. Syst. Appl. 159, 113617 (2020). https://doi.org/10.1016/J.ESWA.2020.113617

    Article  Google Scholar 

  101. Zhang, Q., Leung, Y.W.: An orthogonal genetic algorithm for multimedia multicast routing. IEEE Trans. Evol. Comput. 3, 53–62 (1999). https://doi.org/10.1109/4235.752920

    Article  Google Scholar 

  102. Leung, Y.W., Wang, Y., Leung, Y.W., Wang, Y.: An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE Trans. Evol. Comput. 5, 41–53 (2001). https://doi.org/10.1109/4235.910464

    Article  Google Scholar 

  103. Cuevas, E., Oliva, D., Zaldivar, D., Pajares, G.: Opposition-based electromagnetism-like for global optimization. Int. J. Innov. Comput. Inform. Control 8, 8181–8198 (2012)

    Google Scholar 

  104. Ahandani, M.A.: Opposition-based learning in the shuffled bidirectional differential evolution algorithm. Swarm Evol. Comput. 26, 64–85 (2016). https://doi.org/10.1016/j.swevo.2015.08.002

  105. Banerjee, A., Mukherjee, V., Ghoshal, S.P.: An opposition-based harmony search algorithm for engineering optimization problems. Ain Shams Eng. J. 5, 85–101 (2014). https://doi.org/10.1016/j.asej.2013.06.002

  106. Wang, G.G., Deb, S., Gandomi, A.H., Alavi, A.H.: Opposition-based krill herd algorithm with Cauchy mutation and position clamping. Neurocomputing. 177 (2016). https://doi.org/10.1016/j.neucom.2015.11.018

  107. Ma, X., Liu, F., Qi, Y., Gong, M., Yin, M.: MOEA/D with opposition-based learning for multiobjective optimization problem. Neurocomputing 146, 48–64 (2014). https://doi.org/10.1016/j.neucom.2014.04.068

    Article  Google Scholar 

  108. Premkumar, M., Jangir, P., Sowmya, R., Elavarasan, R.M., Kumar, B.S.: Enhanced chaotic JAYA algorithm for parameter estimation of photovoltaic cell/modules. ISA Trans. 116, 139–166 (2021). https://doi.org/10.1016/j.isatra.2021.01.045

    Article  Google Scholar 

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  1. Department of Electrical and Electronics Engineering, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, 639113, India

    C. Kumar

  2. Department of Electrical and Electronics Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, 641042, India

    D. Magdalin Mary

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

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