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High concentrator photovoltaic module simulation by neuronal networks using spectrally corrected direct normal irradiance and cell temperature

机译:通过神经网络使用光谱校正的直接法向辐照度和电池温度模拟高聚光光伏模块

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The electrical modelling of HCPV (high concentrator photovoltaic) modules is a key issue for systems design and energy prediction. However, the electrical modelling of HCPV modules shows a significantly level of complexity than conventional photovoltaic technology because of the use of multi-junction solar cells and optical devices. In this paper, a method for the simulation of the I-V curves of a HCPV module at any operating condition is introduced. The method is based on three different ANN (artificial neural networks)-based models: one to spectrally correct the direct normal irradiance, one to predict the cell temperature and one to generate the I-V curve of the HCPV module. The method has the advantage that is fully based on atmospheric parameter and outdoor measurements. The analysis of results shows that the method accurately predicts the I-V curve of a HCPV module for a wide range of atmospheric operating conditions with a RMSE (root mean square error) ranging from 0.19% to 1.66% and a MBE "(mean bias error) ranging from -0.38% to 0.40%. (C) 2015 Elsevier Ltd. All rights reserved.
机译:HCPV(高聚光光伏)模块的电气建模是系统设计和能量预测的关键问题。但是,由于使用了多结太阳能电池和光学设备,HCPV模块的电气建模显示出比常规光伏技术高得多的复杂性。本文介绍了一种在任何工作条件下模拟HCPV模块的I-V曲线的方法。该方法基于三种不同的基于ANN(人工神经网络)的模型:一种用于光谱校正直接法向辐照度,一种用于预测细胞温度,另一种用于生成HCPV模块的I-V曲线。该方法的优点是完全基于大气参数和室外测量。结果分析表明,该方法可准确预测HCPV组件在大范围大气条件下的IV曲线,其RMSE(均方根误差)范围为0.19%至1.66%,而MBE“(平均偏差误差)范围从-0.38%到0.40%(C)2015 Elsevier Ltd.保留所有权利。

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