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Explicit empirical model for general photovoltaic devices: Experimental validation at maximum power point

机译:通用光伏设备的显式经验模型:最大功率点的实验验证

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摘要

The validation of a new explicit empirical model for general photovoltaic devices, providing current and voltage at Maximum Power Point (MPP) and current-voltage/power-voltage characteristics under arbitrary conditions of temperature and irradiance, is presented. One of the main advantages of this model is the fact that the equivalent circuit parameters - such as series and shunt resistance, dark-saturation currents, etc. - are not needed, as the sole model input data are the device parameters commonly reported in the datasheets. Moreover, the model is explicit so that its application is very affordable from the computational standpoint. The model is applied to three different types of photovoltaic modules representing some of the most widely diffused technologies in the current market: multi-crystalline silicon, CdTe and CIGS. The calculated voltages, currents and powers at maximum power point are compared with the ones measured for three modules working at the photovoltaic test facility of the University of Trieste. A statistical analysis is presented in order to prove the effectiveness and reliability of the model at maximum power point. Finally, the results of the new explicit model are compared with those obtained by a polynomial regression, Artificial Neural Network (ANN), the well-known single-diode model and an additional, different explicit model. This work shows that the electric performance of a photovoltaic module can be predicted with a high degree of accuracy on the sole basis of parameters that are always found in the photovoltaic device's datasheet.
机译:提出了一种针对常规光伏器件的新显式经验模型的验证,该模型可在任意温度和辐照度条件下提供最大功率点(MPP)处的电流和电压以及电流-电压/功率-电压特性。该模型的主要优点之一是不需要等效电路参数(例如串联电阻和分流电阻,暗饱和电流等),因为唯一的模型输入数据是设备参数中通常报告的参数。数据表。此外,该模型是显式的,因此从计算角度来看,其应用非常经济。该模型被应用于代表当前市场上最广泛使用的技术的三种不同类型的光伏模块:多晶硅,CdTe和CIGS。将计算出的最大功率点处的电压,电流和功率与在的里雅斯特大学的光伏测试设施中工作的三个模块的测量值进行比较。为了证明该模型在最大功率点的有效性和可靠性,提出了统计分析。最后,将新的显式模型的结果与通过多项式回归,人工神经网络(ANN),著名的单二极管模型和其他不同的显式模型获得的结果进行比较。这项工作表明,仅根据光伏设备数据手册中始终存在的参数,就可以高度准确地预测光伏模块的电性能。

著录项

  • 来源
    《Solar Energy》 |2014年第3期|105-116|共12页
  • 作者单位

    Department of Engineering and Architecture, University of Trieste, Via A. Valeria, 6/A, 34127 Trieste, Italy;

    Faculty of Sciences and Technologies, Renewable Energy Laboratory, Jijel University, Jijel 18000, Algeria,Abclus Salam International Centre for Theoretical Physics, Stracla Costiera, 11, 34151 Trieste, Italy;

    Department of Engineering and Architecture, University of Trieste, Via A. Valeria, 6/A, 34127 Trieste, Italy;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Photovoltaic devices; Empirical; Explicit model; Current-power/voltage characteristic; Artificial neural network; Polynomial regression;

    机译:光伏设备;经验;显式模型;电流-功率/电压特性;人工神经网络;多项式回归;

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