PID-like Neural Network for Maximum Power Point Tracking of a Photovoltaic System | ||||
| Menoufia Journal of Electronic Engineering Research | ||||
| Article 6, Volume 28, ICEEM2019-Special Issue, 2019, Page 242-247 | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjeer.2019.64929 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Shaymaa Amer* ; Ahmed M. El-Nagar; Mohammad El-Bardini | ||||
| Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering, Menoufia University Menouf, Egypt | ||||
| Abstract | ||||
| In this study, the PID–like neural network (PIDNN) controller is proposed for maximum power point tracking (MPPT) of the photovoltaic (PV) systems. The proposed neural network (NN) structure works as a nonlinear PID controller. The proposed controller combines the advantages of the PID controller such as easy to implement and the advantages of the NN. The parameters of the proposed PIDNN are learned and tuned on-line based on the gradient descent method. This scheme creates a nonlinear PID controller, which improves the system performance. The results show the robustness of the PIDNN under different levels of the environmental changes such as PV cell temperature and solar irradiance. The simulation results for the proposed PIDNN are compared with other schemes in order to show the robustness of the proposed structure. | ||||
| Keywords | ||||
| Photovoltaic System; Maximum Power Point Tracking; PID controller; Neural Networks | ||||
| References | ||||
|
[1] J. Freeman, “A critical look at the moral case for fossil fuel, ” Energy LJ, vol. 36, pp. 327-338, 2015.
[2] G. Kosmadakis, S. Karellas, and E. Kakaras, “Renewable and conventional electricity generation systems: Technologies and diversity of energy systems,” In Renewable Energy Governance, Springer London, pp. 9-30, 2013.
[3] Y. Chu, and P. Meisen, “Review and comparison of different solar energy technologies,” Global Energy Network Institute (GENI), San Diego, CA, 2011.
[4] J. L. Gray, “The physics of the solar cell. Handbook of Photovoltaic Science and Engineering,” Second Edition, pp. 82-129, 2011.
[5] M.G. Villalva, J.R. Gazoli and E.F.Ruppert, “Comprehensive approach to modeling and simulation of photovoltaic arrays,” IEEE Trans. Power Electron., vol. 25, pp. 1198–1208, 2009.
[6] A. Pallavee Bhatnagar, and B. R. K. Nema, “Conventional and global maximum power point tracking techniques in photovoltaic applications: A review,” Journal of Renewable and Sustainable Energy, vol. 5, 2013.
[7] T. Esram, P.L. “Chapman Comparison of photovoltaic array maximum power point tracking techniques,” IEEE Transactions on Energy Conversion, vol. 22, pp. 439-449, 2007.
[8] E. Koutroulis, K. Kalaitzakis, and N.C. Voulgaris, “Development of a microcontroller-based, photovoltaic maximum power point tracking control system,” IEEE Transactions on Power Electronics ,vol. 16, pp. 46-54, 2001.
[9] S. Lalouni, D. Rekioua, T. Rekioua, and E. Matagne, “Fuzzy logic control of stand-alone photovoltaic system with battery storage,” Journal of Power Sources, vol. 193, pp. 899–907, 2009.
[10] S. Li, H. Liao, H. Yuan, Q. Ai, and K. Chen, “A MPPT strategy with variable weather parameters through analyzing the effect of the DC/DC converter to the MPP of PV system,” Solar Energy, vol. 144, pp. 175-184, 2017.
[11] Y. T. Chen, Y. C. Jhang, and R. H. Liang, “A fuzzy-logic based auto-scaling variable step-size MPPT method for PV systems,” Solar Energy, vol. 126, 53-63, 2016.
[12] C. L. Srinivas, and E. S. Sreeraj, “A Maximum Power Point Tracking Technique Based on Ripple Correlation Control for Single Phase Photovoltaic System with Fuzzy Logic Controller,” Energy Procedia, vol. 90, pp. 69-77, 2016.
[13] B. Bendib, F. Krim, H. Belmili, M.F. Almi, and S. Boulouma, “Advanced fuzzy MPPT controller for a stand-alone PV system,” Energy Procedia, vol. 50, pp. 383-392, 2014.
[14] C.B. Salah, and M. Ouali, “Comparison of fuzzy logic and neural network in maximum power point tracker for PV systems,” Electric Power Systems Research, vol. 81, pp. 43–50, 2011.
[15] A. I. Dounis, P. Kofinas, G. Papadakis, and C. Alafodimos, “A direct adaptive neural control for maximum power point tracking of photovoltaic system,” Solar Energy, vol. 115, pp. 145-165, 2015.
[16] U. Yilmaza , A. Kircaya, and S. Borekcib, “PV system fuzzy logic MPPT method and PI control as a charge controller,” Renewable and Sustainable Energy Reviews, vol. 81, pp. 994–1001, 2018.
[17] A. Youssefa , M. El Telbanya , and A. Zekryb, “Reconfigurable generic FPGA implementation of fuzzy logic controller for MPPT of PV systems,” Renewable and Sustainable Energy Reviews, vol. 82, pp. 1313-1319, 2018.
[18] L.K. Letting, J.L. Munda, and A. Hamam, “Particle swarm optimized T-S fuzzy logic controller for maximum power point tracking in a photovoltaic system,” IEEE, IPEC, pp. 89-94, 2010.
[19] C. Larbes, S.M.A. Cheikh, T. Obeidi, and A. Zerguerras, “Genetic algorithms optimized fuzzy logic control for the maximum power point tracking in photovoltaic system,” Renewable Energy, vol. 34, pp. 2093-2100, 2009.
[20] I. Dounis, P. Kofinas, C. Alafodimos, and D. Tseles, “Adaptive fuzzy gain scheduling PID controller for maximum power point tracking of photovoltaic system,” Renewable energy, vol. 60, pp. 202-214, 2013.
[21] M. Rebhi, A. Benatillah, M. Sellam, and B. Kadri, “Comparative study of MPPT Controllers for PV system implemented in the south-west of algeria,” Energy Procedia, vol. 36, pp. 142-153, 2013.
[22] R. F. Coelho, W. M. dos Santos, and D. C. Martins, “Influence of power converters on PV maximum power point tracking efficiency,” 10th IEEE/IAS International Conference on Industry Applications (INDUSCON), pp. 1-8, 2012.
[23] J. Peng, and R. Dubay, “Identification and adaptive neural network control of a DC motor system with dead-zone characteristics,” ISA Transactions, vol. 50, pp. 588-598, 2011.
[24] J. Tanomaru, and S. Omatu, “Process control by on-line trained neural controllers,” IEEE Transaction on Industrial Electronics, vol. 39, pp. 511-522, 1992. | ||||
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