Modeling of Solar Power Plant Using a Neural Network Based on the Equivalent of a Single Diode
Subject Areas : electrical and computer engineering
Ali Reza reisi
1
(Assistant Professor, Department of Electrical Engineering, Technical and Vocational University (TVU), Tehran, Iran. )
Rohollah Abdollahi
2
(Instructor, Department of Electrical Engineering, Technical and Vocational University (TVU), Tehran, Iran)
Keywords: Modeling, solar power plant, neural network, single diode equivalent circuit,
Abstract :
Various methods have been proposed for modeling solar panels, but modeling solar power plants using them is associated with challenges. In equivalent circuit-based methods, the modeling depends on factory data that changes over time. Modeling of voltage-current characteristic using intelligent methods such as neural network was less considered due to the low accuracy of modeling. In this article, a method independent of the manufacturer's data for modeling the solar power plant is presented, so that it is possible to accurately model the solar power plants that have been installed for several years. The proposed method consists of two steps, in the first step, open circuit voltage, maximum power point and short circuit current are modeled according to atmospheric conditions using neural network. In the second step, the unknown parameters of the equivalent circuit are determined by circuit analysis relations and using neural network outputs. Finally, to evaluate the proposed method, a 3-kW solar power plant was modeled, and the results show the appropriate accuracy of the proposed method for modeling the solar power plant.
[1] D. C. Jordan and S. R. Kurtz, "Field performance of 1.7 GW of photovoltaic systems," IEEE J. of Photovoltaics, vol. 5, no. 1, pp. 243-249, Jan. 2015.
[2] M. Brenna, F. Foiadelli, M. Longo, and D. Zaninelli, "Energy storage control for dispatching photovoltaic power," IEEE Trans. on Smart Grid, vol. 9, no. 4, pp. 2419-2428, Jul. 2018.
[3] S. Hara, H. Douzono, M. Imamura, and Tatsuya Yoshioka, "Estimation of photovoltaic cell parameters using measurement data of photovoltaic module string currents and voltages," IEEE J. of Photovoltaics, vol. 12, no. 2, pp. 540 -545, Mar. 2022.
[4] M. Ma, et al., "Photovoltaic module current mismatch fault diagnosis based on I-V data," IEEE J. of Photovoltaics, vol. 11, no. 3, pp. 779-788, May 2021.
[5] K. Kamil, K. H. Chong, and H. Hashim, "Excess power rerouting in the grid system during high penetration solar photovoltaic," Electric Power Systems Research, vol. 214, Pt. A, Article ID: 108871, Jan. 2023.
[6] P. Chao, et al., "A comprehensive review on dynamic equivalent modeling of large photovoltaic power plants," Solar Energy, vol. 210, pp. 87-100, Nov. 2020.
[7] G. Ciulla, V. L. Brano, V. D. Dio, and G. Cipriani, "A comparison of different one-diode models for the representation of I-V characteristic of a PV cell," Renewable and Sustainable Energy Reviews, vol. 32, pp. 684-696, Apr. 2014.
[8] A. Mellit and S. A. Kalogirou, "Artificial intelligence techniques for photovoltaic applications: a review," Progress in Energy and Combustion Science, vol. 34, no. 5, pp. 574-632, Oct. 2008.
[9] K. Garud, S. Jayaraj, and M. Lee, "A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models," International J. of Energy Research, vol. 45, no. 1, Article ID: 5608, 2020.
[10] F. Almonacid, C. Rus, L. Hontoria, M. Fuentes, and G. Nofuentes, "Review, characterisation of si-crystalline PV modules by artificial neural networks," Renewable Energy, vol. 34, no. 4, pp. 941-949, Apr. 2009.
[11] F. Bonanno, G. Capizzi, G. Graditi, C. Napoli, and G. M. Tina, "A radial basis function neural network-based approach for the electrical characteristics estimation of a photovoltaic module," Applied Energy, vol. 97, pp. 956-961, Sept. 2012.
[12] M. Laurino, M. Piliougine, and G. Spagnuolo, "Artificial neural network based photovoltaic module diagnosis by current-voltage curve classification," Solar Energy, vol. 236, pp. 383-392, Apr. 2022.
[13] C. Huang and P. H. Kuo, "Multiple-input deep convolutional neural network model for short-term photovoltaic power forecasting," IEEE Access, vol. 7, pp. 74822-74834, 2019.
[14] F. M. Khaleel, I. A. Hasan, and M. J. Mohammed, "PV panel system modelling method based on neural network," in Proc. AIP Conf. Proc. 2386, Article ID: 040029, 11 pp., 2022.
[15] Z. Si, Y. Yu, M. Yang, and P. Li, "Hybrid solar forecasting method using satellite visible images and modified convolutional neural networks," IEEE Trans. on Industry Applications, vol. 57, no. 1, pp. 5-16, Jan./Feb. 2021.
[16] W. Zhang, et al., "Deep-learning-based probabilistic estimation of solar PV soiling loss," IEEE Trans. on Sustainable Energy, vol. 12, no. 4, pp. 2436-2444, Oct. 2021.
[17] S. Shongwe and M. Hanif, "Comparative analysis of different single-diode PV modeling methods," IEEE J. of Photovoltaics, vol. 5, no. 3, pp. 938 - 946, May 2015.
[18] V. L. Brano, A. Orioli, G. Ciulla, and A. D. Gangi, "An improved five-parameter model for photovoltaic modules," Solar Energy Materials & Solar Cells, vol. 94, no. 8, pp. 1358-1370, Aug. 2010.
[19] Y. C. Hsieh, et al., "Parameter identification of one-diode dynamic equivalent circuit model for photovoltaic panel," IEEE J. of Photovoltaics, vol. 10, no. 1, pp. 219-225, 2020.
[20] Y. Mahmoud and W. Xiao, "Evaluation of shunt model for simulating photovoltaic modules," IEEE J. of Photovoltaics, vol. 8, no. 6, pp. 1818-1823, Nov. 2018.
[21] E. Karatepe, M. Boztepe, and M. Colak, "Neural network based solar cell model," Energy Conversion and Management, vol. 47, no. 9-10, pp. 1159-1178, Jun. 2006.
[22] A. N. Celik, "Artificial neural network modelling and experimental verification of the operating current of mono-crystalline photovoltaic modules," Solar Energy, vol. 85, no. 10, pp. 2507-2517, Oct. 2011.
[23] X. J. Dong, J. N. Shen, Z. F. Ma, and Y. J. He, "Simultaneous operating temperature and output power prediction method for photovoltaic modules," Energy, vol. 260, Article ID: 124909, Dec. 2022.
[24] M. G. Villalva, J. R. Gazoli, and E. R. Filho, "Comprehensive approach to modeling and simulation of photovoltaic arrays," IEEE Trans. on Pow. Electr., vol. 24, no. 5, pp. 1198-1208, May 2009.