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,
Maswood Abdus Sakur
Affiliations
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
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.
