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...

中文翻译：

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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

准确表征和预测不同运行条件下光伏 (PV) 模块的电流-电压特性对于太阳能功率预测和确保电网稳定性至关重要。由于电流-电压方程是隐式的，基于单二极管模型的传统方法既不方便又复杂。在本文中，提出了一种将人工神经网络 (ANN) 与显式分析模型 (EAM) 相结合的新方法，用于预测不同运行条件下光伏组件的 IV 特性。由于 EAM 的简单性和明确的表达，它可以有效地从估计的模型参数中获得 IV 曲线。基于EAM的人工神经网络由三层前馈神经网络组成，其中输入是太阳辐射和模块温度，输出是 EAM 中的四个参数。一旦使用测量的 IV 曲线构建和训练了 ANN，形状参数和 IV 曲线就可以通过仅读取太阳辐射和温度而不求解任何非线性隐式方程来预测。通过不同类型光伏组件的实验数据验证了所提出方法的准确性和能力。此外，首先研究了 EAM 中形状参数对太阳辐射和温度的依赖性。准确表征和预测不同运行条件下光伏 (PV) 模块的电流-电压特性对于太阳能功率预测和确保电网稳定性至关重要。由于电流-电压方程是隐式的，基于单二极管模型的传统方法既不方便又复杂。在本文中，提出了一种将人工神经网络 (ANN) 与显式分析模型 (EAM) 相结合的新方法，用于预测不同运行条件下光伏组件的 IV 特性。由于 EAM 的简单性和明确的表达，它可以有效地从估计的模型参数中获得 IV 曲线。基于EAM的人工神经网络由三层前馈神经网络组成，其中输入为太阳辐射和组件温度，输出为EAM中的四个参数。一旦使用测量的 IV 曲线构建和训练 ANN 后，形状参数和 IV 曲线就可以通过...