Abstract
Maximum Power Point Tracking (MPPT) is one of the most challenging aspects of Photovoltaic (PV) system design. In fact, to improve the efficiency of solar panels, a viable MPPT approach is necessary. Many of these techniques are slow and imprecise in terms of functionality. The purpose of this paper is to give a performance study of a new configuration of Artificial Neural Network (ANN) models based on the Bayesian Regularization (BR) training algorithm, with the goal of outperforming the most widely used MPPT techniques. Consequently, the suggested approach based on the ANN-BR algorithm has been trained and analyzed for multiple model topologies, with the best generated configuration containing 19 neurons achieving 99.9997 % accuracy. In addition, it has shown an excellent power output convergence by reaching 99.9763 % of the PV’s Maximum Power Point (MPP), a better perturbation reduction, and a fast tracking speed of 37 ms compared to the most applicable MPPT algorithms, notably Perturb & Observe (P &O), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA). The obtained results have been evaluated using the Mean Square Error (MSE) and the Root Mean Square Error (RMSE) fitness functions, and the suggested algorithm’s potency and efficiency are examined using flow simulations in the MATLAB ®software.
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Al-Shahri, O. A., Ismail, F. B., Hannan, M., et al. (2021). Solar photovoltaic energy optimization methods, challenges and issues: a comprehensive review. Journal of Cleaner Production, 284(125), 465.
Al-Showany, E. F. A. (2016). The impact of the environmental condition on the performance of the photovoltaic cell. American Journal of Energy Engineering, 4(1), 1–7.
Alonso-Montesinos, J., Ballestrín, J., López, G., et al. (2021). The use of ann and conventional solar-plant meteorological variables to estimate atmospheric horizontal extinction. Journal of Cleaner Production, 285(125), 395.
Baimel, D., Tapuchi, S., Levron, Y., et al. (2019). Improved fractional open circuit voltage mppt methods for pv systems. Electronics, 8(3), 321.
Baimel, D., Tapuchi, S., Levron, Y., et al. (2019). Improved fractional open circuit voltage mppt methods for pv systems. Electronics, 8(3), 321.
Banakhr, F. A., & Mosaad, M. I. (2021). High performance adaptive maximum power point tracking technique for off-grid photovoltaic systems. Scientific Reports, 11(1), 1–13.
Bhan, V., Shaikh, S. A., Khand, Z. H., et al. (2021). Performance evaluation of perturb and observe algorithm for mppt with buck-boost charge controller in photovoltaic systems. Journal of Control, Automation and Electrical Systems, 32(6), 1652–1662.
Burden, F. and Winkler, D. (2008). Bayesian regularization of neural networks. Artificial neural networks pp 23–42.
Çelik, E., Gör, H., Öztürk, N., et al. (2017). Application of artificial neural network to estimate power generation and efficiency of a new axial flux permanent magnet synchronous generator. International Journal of Hydrogen Energy, 42(28), 17,692-17,699. https://doi.org/10.1016/j.ijhydene.2017.01.168 special Issue on The 4th European Conference on Renewable Energy Systems (ECRES 2016), 28-31 August 2016, Istanbul, Turkey.
Çelik, E., H, et al. (2018). Modelling of the clearance effects in the blanking process of cuzn30 sheet metal using neural network- a comparative study. Bilişim Teknolojileri Dergisi, 11(2), 187–193.
Çelik, E., Uzun, Y., Kurt, E., et al. (2018). A neural network design for the estimation of nonlinear behavior of a magnetically-excited piezoelectric harvester. Journal of Electronic Materials, 47(8), 4412–4420.
Chander, S., Purohit, A., Sharma, A., et al. (2015). Impact of temperature on performance of series and parallel connected mono-crystalline silicon solar cells. Energy Reports, 1, 175–180.
Chatterjee, P., Ambati, M. S. K., Chakraborty, A. K., et al. (2022). Photovoltaic/photo-electrocatalysis integration for green hydrogen: A review. Energy Conversion and Management, 261(115), 648. https://doi.org/10.1016/j.enconman.2022.115648
Chellaswamy C, Shaji M, Jawwad M, et al (2019) A novel optimization method for parameter extraction of industrial solar cells. In: 2019 innovations in power and advanced computing technologies (i-PACT), IEEE, pp 1–6
Cortés, B., Sánchez, R. T., & Flores, J. J. (2020). Characterization of a polycrystalline photovoltaic cell using artificial neural networks. Solar Energy, 196, 157–167.
Day, J., Senthilarasu, S., & Mallick, T. K. (2019). Improving spectral modification for applications in solar cells: A review. Renewable Energy, 132, 186–205.
Deotti, L., Silva Júnior, I., Honório, L., et al. (2021). Empirical models applied to distributed energy resources-an analysis in the light of regulatory aspects. Energies, 14(2), 326.
Dhass, A. D., Kumar, R. S., Lakshmi, P., et al. (2020). An investigation on performance analysis of different pv materials. Materials Today: Proceedings, 22, 330–334.
Farh, H. M., Eltamaly, A. M., & Othman, M. F. (2018). Hybrid pso-flc for dynamic global peak extraction of the partially shaded photovoltaic system. PloS one, 13(11), e0206,171.
Furkan D, Mehmet Emin M (2010) Critical factors that affecting efficiency of solar cells. smart grid and renewable energy 2010
Gouabi, H., Hazzab, A., Habbab, M., et al. (2021). Experimental implementation of a novel scheduling algorithm for adaptive and modified p &o mppt controller using fuzzy logic for wecs. International Journal of Adaptive Control and Signal Processing, 35(9), 1732–1753.
Haghnegahdar L, Amjadi Z (2019) A cyber-resilience trend for data classification in scada system with applying pso in bayesian regularization neural network. In: IIE Annual Conference. Proceedings, Institute of Industrial and Systems Engineers (IISE), pp 106–111
Jain, A., Sharma, S., & Kapoor, A. (2006). Solar cell array parameters using lambert w-function. Solar Energy Materials and Solar Cells, 90(1), 25–31.
Javed, M. Y., Mirza, A. F., Hasan, A., et al. (2019). A comprehensive review on a pv based system to harvest maximum power. Electronics, 8(12), 1480.
Kayri, M. (2016). Predictive abilities of bayesian regularization and levenberg-marquardt algorithms in artificial neural networks: a comparative empirical study on social data. Mathematical and Computational Applications, 21(2), 20.
Khan, F. A., Pal, N., & Saeed, S. H. (2018). Review of solar photovoltaic and wind hybrid energy systems for sizing strategies optimization techniques and cost analysis methodologies. Renewable and Sustainable Energy Reviews, 92, 937–947.
Kumar C, Rao RS (2016) A novel global mpp tracking of photovoltaic system based on whale optimization algorithm. International Journal of Renewable Energy Development 5(3)
Kumar, M. V., Mogili, A. R., Anusha, S., et al. (2021). A new fuzzy based inc-mppt algorithm for constant power generation in pv systems. Intern Res J Eng Tech, 8, 212–217.
Kumar, V., Kumar, A., Dhasmana, H., et al. (2018). Efficiency enhancement of silicon solar cells using highly porous thermal cooling layer. Energy & Environment, 29(8), 1495–1511.
LeCun, Y., Bottou, L., Bengio, Y., et al. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.
Majid, Z., Ruslan, M., Sopian, K., et al. (2014). Study on performance of 80 watt floating photovoltaic panel. Journal of Mechanical Engineering and Sciences, 7(1), 1150–1156.
Memaya, M., Moorthy, C. B., Tahiliani, S., et al. (2019). Machine learning based maximum power point tracking in solar energy conversion systems. International Journal of Smart Grid and Clean Energy, 8(6), 662–9.
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46–61.
Motahhir, S., El Hammoumi, A., & El Ghzizal, A. (2020). The most used mppt algorithms: Review and the suitable low-cost embedded board for each algorithm. Journal of cleaner production, 246(118), 983.
Naderi E, Asrari A (2021a) Experimental validation of grid-tied and standalone inverters on a lab-scale wind-pv microgrid. In: 2021 IEEE International Power and Renewable Energy Conference (IPRECON), IEEE, pp 1–6
Naderi E, Asrari A (2021b) Hardware-in-the-loop experimental validation for a lab-scale microgrid targeted by cyberattacks. In: 2021 9th international conference on smart grid (icSmartGrid), IEEE, pp 57–62
Naderi, E., Bibek, K., Ansari, M., et al. (2021). Experimental validation of a hybrid storage framework to cope with fluctuating power of hybrid renewable energy-based systems. IEEE Transactions on Energy Conversion, 36(3), 1991–2001.
Ncir, N., Sebbane, S., & El Akchioui, N. (2022). A novel intelligent technique based on metaheuristic algorithms and artificial neural networks: Application on a photovoltaic panel. 2022 2nd International Conference on Innovative Research in Applied Science (pp. 1–8). IEEE: Engineering and Technology (IRASET).
Nishioka, K., Takamoto, T., Agui, T., et al. (2006). Evaluation of ingap/ingaas/ge triple-junction solar cell and optimization of solar cell’s structure focusing on series resistance for high-efficiency concentrator photovoltaic systems. Solar Energy Materials and Solar Cells, 90(9), 1308–1321.
Noamane N, Saliha S, El Akchioui N (2022) Comparison of the efficiency of ann training algorithms for tracking the maximum power point of photovoltaic field. In: International conference on electrical systems & Automation, Springer, pp 21–31
Pan, H., Niu, X., Li, R., et al. (2020). Annealed gradient descent for deep learning. Neurocomputing, 380, 201–211.
Pranava G, Prasad P (2013) Constriction coefficient particle swarm optimization for economic load dispatch with valve point loading effects. In: 2013 international conference on power, energy and control (ICPEC), IEEE, pp 350–354
Qerimi, D., Dimitrieska, C., Vasilevska, S., et al. (2020). Modeling of the solar thermal energy use in urban areas. Civil Engineering Journal, 6(7), 1349–1367.
Rezaei, M. M., & Asadi, H. (2019). A modified perturb-and-observe-based maximum power point tracking technique for photovoltaic energy conversion systems. Journal of Control, Automation and Electrical Systems, 30(5), 822–831.
Rodrigo, H. S. (2017). Bayesian artificial neural networks in health and cybersecurity. University of South Florida.
Roy, R. B., Rokonuzzaman, M., Amin, N., et al. (2021). A comparative performance analysis of ann algorithms for mppt energy harvesting in solar pv system. IEEE Access, 9, 102137–102,152.
Sammut, C., & Webb, G. I. (2011). Encyclopedia of machine learning. Springer Science & Business Media.
Sariev, E., & Germano, G. (2020). Bayesian regularized artificial neural networks for the estimation of the probability of default. Quantitative Finance, 20(2), 311–328.
Sariev, E., & Germano, G. (2020). Bayesian regularized artificial neural networks for the estimation of the probability of default. Quantitative Finance, 20(2), 311–328.
Sebbane S, Ncir N, El Akchioui N (2022a) Diagnosis and classification of photovoltaic panel defects based on a hybrid intelligent method. In: international conference on electrical systems & automation, Springer, pp 59–69
Sebbane, S., Ncir, N., & El Akchioui, N. (2022b). Performance study of artificial neural network training algorithms for classifying pv field defects. 2022 2nd International Conference on innovative research in applied Science (pp. 1–5). IEEE: engineering and technology (IRASET).
Sedaghati F, Nahavandi A, Badamchizadeh MA, et al (2012) Pv maximum power-point tracking by using artificial neural network. Mathematical Problems in Engineering 2012
Seyedmahmoudian, M., Horan, B., Soon, T. K., et al. (2016). State of the art artificial intelligence-based mppt techniques for mitigating partial shading effects on pv systems-a review. Renewable and Sustainable Energy Reviews, 64, 435–455.
Shukla, A., Kant, K., Sharma, A., et al. (2017). Cooling methodologies of photovoltaic module for enhancing electrical efficiency: A review. Solar Energy Materials and Solar Cells, 160, 275–286.
Soler-Castillo, Y., Rimada, J. C., Hernández, L., et al. (2021). Modelling of the efficiency of the photovoltaic modules: Grid-connected plants to the cuban national electrical system. Solar Energy, 223, 150–157.
Sredenšek, K., Štumberger, B., Hadžiselimović, M., et al. (2021). Experimental validation of a thermo-electric model of the photovoltaic module under outdoor conditions. Applied Sciences, 11(11), 5287.
Titri, S., Larbes, C., Toumi, K. Y., et al. (2017). A new mppt controller based on the ant colony optimization algorithm for photovoltaic systems under partial shading conditions. Applied Soft Computing, 58, 465–479.
Tobnaghi, D. M., Madatov, R., & Naderi, D. (2013). The effect of temperature on electrical parameters of solar cells. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2(12), 6404–6407.
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Ncir, N., El Akchioui, N. An Intelligent Improvement Based on a Novel Configuration of Artificial Neural Network Model to Track the Maximum Power Point of a Photovoltaic Panel. J Control Autom Electr Syst 34, 363–375 (2023). https://doi.org/10.1007/s40313-022-00972-5
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DOI: https://doi.org/10.1007/s40313-022-00972-5