Improvement and validation of a model for photovoltaic array performance
Improvement and validation of a model for photovoltaic array performance
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DOI：
10.1016/j.solener.2005.06.010
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发表时间：
2006-01-01
期刊：
影响因子：
6.7
通讯作者：
Beckman, WA
中科院分区：
文献类型：
--
作者：
De Soto, W;Klein, SA;Beckman, WA
光伏电池板制造商通常只在一种运行条件下提供电气参数。光伏电池板的运行条件范围很广，因此制造商的信息不足以确定其整体性能。设计师需要一个可靠的工具来预测光伏电池板在所有条件下的发电量，以便做出是否采用这项技术的合理决定。桑迪亚国家实验室已经开发出一种预测能源产量的模型，但它需要的输入数据通常无法从制造商那里获得。本文描述的五参数模型使用制造商提供的数据、吸收的太阳辐射和电池温度，并结合半经验公式来预测电流-电压曲线。本文介绍了如何确定五参数模型的参数，并将预测的电流-电压曲线与来自美国国家标准与技术研究所(NIST)的建筑集成光伏设施的实验数据进行了比较，四种不同的电池技术(单晶、多晶、硅薄膜和三结非晶)。文中还给出了用Sandia模型得到的结果。五参数模型的预测结果与Sandia模型的结果和NIST对所有四种类型电池在一系列运行条件下的测量结果都显示出很好的一致性。五参数模型很有意义，因为它只需要制造商提供的少量输入数据，因此它为能源预测提供了一个有价值的工具。如果制造商的数据包括两个辐射水平的信息，预测能力可能会得到改善。(C)2005爱思唯尔有限公司。保留所有权利。
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. (c) 2005 Elsevier Ltd. All rights reserved.