Abstract
The use of maximum power point tracking techniques in photovoltaic systems attracts particular attention to research and ensures that the photovoltaic energy system delivers as much as possible of the output power available to the load, regardless of the climatic conditions (variation in temperature and solar radiation), the choice and development are made to implement a more appropriate and effective maximum power point tracking controller using neural networks.
In order to obtain maximum power point tracking, the importance has also been given in this paper to the photovoltaic panel, these inputs (temperature and solar radiation) and also to the control of the power converter.
The results obtained using the Matlab/Simulink environment; confirm the effectiveness of the proposed method in terms of efficiency, fast calculation time of its robustness and the precision of its outputs which allow giving decisions correct, reliable and immediate.
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Sourov, M.R., Tania Ahmed, U., Golam Rabbani, M.A.: High performance maximum power point tracker for photovoltaic power system using DC-DC boost converter. IOSR J. Eng. 2(12), 12–20 (2012)
Mahjoub-Essefi, R., Souissi, M., Hadj-Abdallah, H.: Maximum power point tracking control technique for photovoltaic systems using neural networks. In: Proceedings of the 5th Annual International Renewable Energy Congress, IREC’2014, Hammamet, 27 March, pp. 22–427 (2014)
Mahjoub Essefi, R., Souissi, M., Hadj Abdallah, H.: Maximum power point tracking control using neural networks for stand-alone photovoltaic systems. Int. J. Modern Nonlinear Theory Appl. 3, 53–65 (2014)
Almonacid, F., Fernandez, E.F., Rodrigo, P., Pérez-Higueras, P.J., Rus-Cascas, C.: Estimating the maximum power of a high concentrator photovoltaic (HCPV) module using an artificial neural network. Energy 53, 165–172 (2013)
Mahanta, J., Sharma, B., Sarmah, N.: A review of maximum power point tracking algorithm for solar photovoltaic applications. J. Electr. Electron. Eng. (IOSR-JEEE) 13(4), 01–13 (2018)
Amoozadeh, M., Gholamian, S.A.: Active and reactive power control of photovoltaic systems connected to the network for maximum power point tracking. Int. J. Mechatron. Electr. Comput. Technol. (IJMEC) 4(12), 857–885 (2014)
Kishor, N., Mohanty, S.R., Villalva, M.G., Ruppert, E.: Simulation of PV array output power for modified PV cell model. In: IEEE, International Conference on Power and Energy (PEC), Kuala Lumpur, Malaysia (2010)
Guingane, T.T., Koalaga, Z., Simonguy, E., Zougmore, F., Bonkoungou, D.: Modélisation et simulation d’un champ photovoltaïque utilisant un convertisseur élévateur de tension (boost) avec le logiciel MATLAB/SIMULINK. Journal International de Technologie, de l’Innovation, de la Physique, de l’Energie et de l’Environnement JITIPEE. 2(1), 1–14 (2016)
Aouchiche, N., Aït Cheikh, M.S., Malek, A.: Poursuite du point de puissance maximale d’un système photovoltaïque par les méthodes de l’incrémentation de conductance et la perturbation & observation. Revue des Energies Renouvelables 16(3), 485–498 (2013)
Kumari, J.S., Sai Babu, Ch., Babu, K.: Design and Analysis of P&O and IP&O MPPT techniques for photovoltaic system. Int. J. Modern Eng. Res. (IJMER) 2(4), 2174–2180 (2012)
Remy, Gh., Bethoux, O., Marchand, C., Dogan, H.: Review of MPPT Techniques for Photovoltaic Systems. Laboratoire de Génie Electrique de Paris (LGEP)/SPEE-Labs, CNRS UMR 8507, SUPELEC; Université Pierre et Marie Curie, France, pp. 1–14
Mammar, K., Chacker, A.: Adaptive neuro-fuzzy controller of PEM fuel cell system power generation. Int. J. Comput. Sci. (IJCSI) 9(6), 1694–081 (2012)
Bendib, B., Krim, F., Belmili, H., Almi, M., Bolouma, F.S.: An intelligent MPPT approach based on neural-network voltage estimator and fuzzy controller, applied to a stand-alone PV system. In: IEEE 23rd International Symposium on Industrial Electronics (ISIE), Istumbul, Turkey, pp. 404–409 (2014)
Manas, M., Kumari, A., Das, S., et al.: An artificial neural network based maximum power point tracking method for photovoltaic system. In: IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE), Jaipur, India (2016)
Chen, M., Ma, S., Wu, J., Huang, L.: Analysis of MPPT failure and development of an augmented nonlinear controller for MPPT of photovoltaic systems under partial shading conditions. J. Appl. Sci. (MDPI) 7(95), 1–22 (2017)
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Saadaoui, F., Mammar, K., Hazzab, A. (2020). The Pursuit of the Maximum Power Point of a Photovoltaic System Using Artificial Neural Network. In: Hatti, M. (eds) Smart Energy Empowerment in Smart and Resilient Cities. ICAIRES 2019. Lecture Notes in Networks and Systems, vol 102. Springer, Cham. https://doi.org/10.1007/978-3-030-37207-1_11
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DOI: https://doi.org/10.1007/978-3-030-37207-1_11
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