Performance prediction of PV modules based on artificial neural network and explicit analytical model
Performance prediction of PV modules based on artificial neural network and explicit analytical model
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基于人工神经网络和显式分析模型的光伏组件性能预测
DOI：
10.1063/1.5131432
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发表时间：
2020
影响因子：
2.5
通讯作者：
Ming Yang
中科院分区：
文献类型：
--
作者：
Chen Zhang;Yunpeng Zhang;Jialei Su;T. Gu;Ming Yang
准确表征和预测光伏组件在不同运行条件下的电流-电压特性对于预测太阳能发电量和保证电网稳定性至关重要。传统的基于单二极管模型的电流-电压方程是隐式的，计算方法不方便且复杂。本文提出了一种将人工神经网络（ANN）与显式分析模型（EAM）相结合的方法，用于预测光伏组件在不同运行条件下的I-V特性。由于EAM的简单和明确的表达，它使得它有效地获得I-V曲线从估计的模型参数。基于EAM的人工神经网络由三层前馈神经网络组成，输入为太阳辐照度和组件温度，输出为EAM中的四个参数。利用实测的I-V曲线建立和训练神经网络后，只需阅读太阳辐射和温度，无需求解任何非线性隐式方程组，即可预测形状参数和I-V曲线。不同类型光伏组件的实验数据验证了该方法的准确性和能力。此外，还研究了EAM中形状参数对太阳辐照度和温度的依赖性，准确表征和预测光伏组件在不同运行条件下的电流-电压特性对于预测太阳能发电量和保证电网稳定性至关重要。传统的基于单二极管模型的电流-电压方程是隐式的，计算方法不方便且复杂。本文提出了一种将人工神经网络（ANN）与显式分析模型（EAM）相结合的方法，用于预测光伏组件在不同运行条件下的I-V特性。由于EAM的简单和明确的表达，它使得它有效地获得I-V曲线从估计的模型参数。基于EAM的人工神经网络由三层前馈神经网络组成，输入为太阳辐照度和组件温度，输出为EAM中的四个参数。利用实测的I-V曲线建立和训练神经网络，只需用神经网络的参数就可以预测形状参数和I-V曲线。
The accurate characterization and prediction of current-voltage characteristics of photovoltaic (PV) modules under different operating conditions is essential for solar power forecasting and ensuring grid stability. The traditional method based on the single-diode model is inconvenient and complex because the current-voltage equation is implicit. In this paper, a novel method combining an artificial neural network (ANN) with an explicit analytical model (EAM) is proposed for predicting the I-V characteristics of PV modules under different operating conditions. The EAM makes it efficient to obtain the I-V curves from the estimated model parameters due to its simplicity and explicit expression. The ANN based on the EAM is composed of a three-layer feedforward neural network, in which the inputs are solar irradiation and module temperature and the outputs are the four parameters in EAM. Once the ANN is built and trained by using the measured I-V curves, the shape parameters and I-V curve are predicted by only reading solar irradiation and temperature without solving any nonlinear implicit equations. The accuracy and capability of the proposed method are verified by the experimental data for different types of PV modules. Moreover, the dependence of shape parameters in the EAM on solar irradiation and temperature is investigated first.The accurate characterization and prediction of current-voltage characteristics of photovoltaic (PV) modules under different operating conditions is essential for solar power forecasting and ensuring grid stability. The traditional method based on the single-diode model is inconvenient and complex because the current-voltage equation is implicit. In this paper, a novel method combining an artificial neural network (ANN) with an explicit analytical model (EAM) is proposed for predicting the I-V characteristics of PV modules under different operating conditions. The EAM makes it efficient to obtain the I-V curves from the estimated model parameters due to its simplicity and explicit expression. The ANN based on the EAM is composed of a three-layer feedforward neural network, in which the inputs are solar irradiation and module temperature and the outputs are the four parameters in EAM. Once the ANN is built and trained by using the measured I-V curves, the shape parameters and I-V curve are predicted by onl...