Abstract
The increasing use of non-renewable sources to produce power energy is raising major environmental concerns. To meet this challenge, the use of alternative sources of energy has being developed, such as solar energy which is widely used for power generation due to the abundant availability of solar radiation and its minimal pollution impact. However, reducing the cost and improving the harnessing efficiency of natural sources use is very important in boosting power plant performance. Therefore, many solutions have emerged and, the maximum power point tracking (MPPT) system is most frequently employed for solar energy. It is used to maximize the production of electrical energy from photovoltaic panels. The aim of this paper is to evaluate the performance of MPPT using an artificial neural network (ANN) method that is used with a DC-DC boost converter to provide constant output to a load in a photovoltaic system. The duty cycle is generated by the ANN algorithm, based on irradiation conditions and temperature changes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Harrag, A., Messalti, S.: Variable step size modified P&O MPPT algorithm using GA-based hybrid offline/online PID controller. Renew. Sustain. Energy Rev. 49, 1247–1260 (2015). https://doi.org/10.1016/j.rser.2015.05.003
Sunny, M.S.H., Ahmed, A.N.R., Hasan, M.K.: Design and simulation of maximum power point tracking of photovoltaic system using ANN. In: 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), 1–5 Sept 2016
Benchikh, S., Jarou, T., Nasri, E., Roa, E.: Design of an adaptive neuro fuzzy inference system for photovoltaic system. In: Farhaoui, Y., Rocha, A., Brahmia, A., Bhushab, B. (eds.) Artificial Intelligence and Smart Environment, pp. 352–358. Springer, Switzerland (2023)
Taherbaneh, M., Rezaie, A.H., Ghafoorifard, H., , K. Rahimi, Menhaj, M.B.: Maximizing output power of a solar panel via combination of sun tracking and maximum power point tracking by fuzzy controllers. Int. J. Photoenergy 2010, 13 (2010). https://doi.org/10.1155/2010/312580
Fawzan, S.: Parameters estimation of photovoltaic modules: comparison of ANN and ANFIS. Int. J. Ind. Electron. Drives 1(2) (2014)
Acknowledgments
BENCHIKH Salma thanks the CNRST for sponsoring research activities.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Benchikh, S., Jarou, T., Boutahir, M.K., Nasri, E., Lamrani, R. (2024). Design of Artificial Neural Network Controller for Photovoltaic System. In: Farhaoui, Y., Hussain, A., Saba, T., Taherdoost, H., Verma, A. (eds) Artificial Intelligence, Data Science and Applications. ICAISE 2023. Lecture Notes in Networks and Systems, vol 838. Springer, Cham. https://doi.org/10.1007/978-3-031-48573-2_81
Download citation
DOI: https://doi.org/10.1007/978-3-031-48573-2_81
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-48572-5
Online ISBN: 978-3-031-48573-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)