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 / S. Ferrari, M. Lazzaroni, V. Piuri, A. Salman, L. Cristaldi, M. Faifer, S. Toscani - In: 2014 IEEE International instrumentation and measurement technology conference : instrumentation and measurement for sustainable development (I2MTC 2014) : proceedings : 12-15 May 2014, Montevideo, UruguayPiscataway (New Jersey) : Institute of electrical and electronics engineers, 2014. - ISBN 9781467363853. - pp. 1261-1266 (( convegno International instrumentation and measurement technology conference: instrumentation and measurement for sustainable development tenutosi a Montevideo (Uruguay) nel 2014 [10.1109/I2MTC.2014.6860947].
A computational intelligence approach to solar panel modelling
S. FerrariPrimo
;M. LazzaroniSecondo
;V. Piuri;
2014
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.File | Dimensione | Formato | |
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