The power produced by a solar panel depends on several parameters. In order to optimize the production, the ability to operate in the Maximum Power Point (MPP) condition is requested. The ability to identify and reach the MPP condition is therefore critical to an efficient conversion of the photovoltaic energy. In this paper, several computational intelligence paradigms are challenged in the task of identifying the MPP power from the working condition directly measurable from the solar panel, such as the voltage, V, the current, I, and the temperature, T, of the panel.

A computational intelligence approach to solar panel modelling

CRISTALDI, LOREDANA;FAIFER, MARCO;TOSCANI, SERGIO
2014-01-01

Abstract

The power produced by a solar panel depends on several parameters. In order to optimize the production, the ability to operate in the Maximum Power Point (MPP) condition is requested. The ability to identify and reach the MPP condition is therefore critical to an efficient conversion of the photovoltaic energy. In this paper, several computational intelligence paradigms are challenged in the task of identifying the MPP power from the working condition directly measurable from the solar panel, such as the voltage, V, the current, I, and the temperature, T, of the panel.
2014
2014 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings
9781467363860
ELETTRICI
Training, Computational modeling, Current measurement, Temperature measurement, Predictive models, Testing, Computational intelligence
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/845536
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