Maximum Power Point Tracking of PV System Based on Machine Learning

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 pa...

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Bibliographic Details
Published inEnergies (Basel) Vol. 13; no. 3; p. 692
Main Authors Takruri, Maen, Farhat, Maissa, Barambones, Oscar, Ramos-Hernanz, José Antonio, Turkieh, Mohammed Jawdat, Badawi, Mohammed, AlZoubi, Hanin, Abdus Sakur, Maswood
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.02.2020
Subjects
Algorithms
boost-converter
Converters
Disturbances
Efficiency
Electric potential
Energy
estimator
forecasting
general regression neural network
General regression neural networks
Irradiation
Learning algorithms
Load
Machine learning
Maximum power tracking
Neural networks
Optimization
Photovoltaic cells
photovoltaic power system
Photovoltaics
pid controller
renewable energy
Support vector machines
support vector regression
Transistors
Voltage
Voltage converters (DC to DC)
Weather
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en13030692