International Scientific Journal


This paper proposes a gray-box approach to modeling and simulation of photo-voltaic modules. The process of building a gray-box model is split into two components (known, and unknown or partially unknown). The former is based on physical principles while the latter relies on functional approximator and data-based modeling. In this paper, artificial neural networks were used to construct the functional approximator. Compared to the standard mathematical model of photovoltaic module which involves the three input variables - solar irradiance, ambient temperature, and wind speed- a gray-box model allows the use of additional input environmental variables, such as wind direction, atmospheric pressure, and humidity. In order to improve the accuracy of the gray-box model, we have proposed two criteria for the classification of the daily input/output data whereby the former determines the season while the latter classifies days into sunny and cloudy. The accuracy of this model is verified on the real-life photo-voltaic generator, by comparing with single-diode mathematical model and artificial neural networks model towards measured output power data. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. III-42009]
PAPER REVISED: 2016-12-19
PAPER ACCEPTED: 2017-02-07
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© 2018 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence