Maximum Power Point Tracking of PV System Based on Machine Learning

Maen Takruri, Maissa Farhat, Oscar Barambones, José Antonio Ramos-Hernanz, Mohammed Jawdat Turkieh, Mohammed Badawi, Hanin AlZoubi and Maswood Abdus Sakur
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Maen Takruri: Department of Electrical, Electronics and Communications Engineering, American University of Ras Al Khaimah, Ras Al Khamah, UAE
Maissa Farhat: Department of Electrical, Electronics and Communications Engineering, American University of Ras Al Khaimah, Ras Al Khamah, UAE
Oscar Barambones: Systems Engineering and Automatic Control Department, Faculty of Engineering Vitoria-Gasteiz, University of the Basque Country, 01006 Vitoria-Gasteiz, Spain
José Antonio Ramos-Hernanz: Electrical Engineering Department, Faculty of Engineering Vitoria-Gasteiz, University of the Basque Country, 01006 Vitoria-Gasteiz, Spain
Mohammed Jawdat Turkieh: Department of Electrical, Electronics and Communications Engineering, American University of Ras Al Khaimah, Ras Al Khamah, UAE
Mohammed Badawi: Department of Electrical, Electronics and Communications Engineering, American University of Ras Al Khaimah, Ras Al Khamah, UAE
Hanin AlZoubi: Department of Electrical, Electronics and Communications Engineering, American University of Ras Al Khaimah, Ras Al Khamah, UAE
Maswood Abdus Sakur: Department of Electrical, Electronics and Communications Engineering, American University of Ras Al Khaimah, Ras Al Khamah, UAE

Energies, 2020, vol. 13, issue 3, 1-14

Abstract: This project studies the conditions at which the maximum power point of a photovoltaic (PV) panel is obtained. It shows that the maximum power point is very sensitive to external disturbances such as temperature and irradiation. It introduces a novel method for maximizing the output power of a PV panel when connected to a DC/DC boost converter under variable load conditions. The main contribution of this work is to predict the optimum reference voltage of the PV panel at all-weather conditions using machine learning strategies and to use it as a reference for a Proportional-Integral-Derivative controller that ensures that the DC/DC boost converter provides a stable output voltage and maximum power under different weather conditions and loads. Evaluations of the proposed system, which uses an experimental photovoltaic dataset gathered from Spain, prove that it is robust against internal and external disturbances. They also show that the system performs better when using support vector machines as the machine learning strategy compared to the case when using general regression neural networks.

Keywords: forecasting; support vector regression; general regression neural network; photovoltaic power system; renewable energy; estimator; boost-converter; PID controller (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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