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Improvement and Validation of a Model for Photovoltaic Array Performance

Published on Jan 1, 2006in Solar Energy4.67
· DOI :10.1016/j.solener.2005.06.010
W. De Soto2
Estimated H-index: 2
(UW: University of Wisconsin-Madison),
Sanford Klein48
Estimated H-index: 48
(UW: University of Wisconsin-Madison),
William A. Beckman42
Estimated H-index: 42
(UW: University of Wisconsin-Madison)
Abstract
Abstract Manufacturers of photovoltaic panels typically provide electrical parameters at only one operating condition. Photovoltaic panels operate over a large range of conditions so the manufacturer’s information is not sufficient to determine their overall performance. Designers need a reliable tool to predict energy production from a photovoltaic panel under all conditions in order to make a sound decision on whether or not to incorporate this technology. A model to predict energy production has been developed by Sandia National Laboratory, but it requires input data that are normally not available from the manufacturer. The five-parameter model described in this paper uses data provided by the manufacturer, absorbed solar radiation and cell temperature together with semi-empirical equations, to predict the current–voltage curve. This paper indicates how the parameters of the five-parameter model are determined and compares predicted current–voltage curves with experimental data from a building integrated photovoltaic facility at the National Institute of Standards and Technology (NIST) for four different cell technologies (single crystalline, poly crystalline, silicon thin film, and triple-junction amorphous). The results obtained with the Sandia model are also shown. The predictions from the five-parameter model are shown to agree well with both the Sandia model results and the NIST measurements for all four cell types over a range of operating conditions. The five-parameter model is of interest because it requires only a small amount of input data available from the manufacturer and therefore it provides a valuable tool for energy prediction. The predictive capability could be improved if manufacturer’s data included information at two radiation levels.
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