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首页> 外文期刊>Simulation modelling practice and theory: International journal of the Federation of European Simulation Societies >ANN based simulation and experimental verification of analytical four- and five-parameters models of PV modules
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ANN based simulation and experimental verification of analytical four- and five-parameters models of PV modules

机译：基于人工神经网络的光伏组件四参数和五参数解析模型的仿真与实验验证

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摘要

In this article, artificial neural network (ANN) is adopted to predict photovoltaic (PV) panel behaviors under realistic weather conditions. ANN results are compared with analytical four and five parameter models of PV module. The inputs of the models are the daily total irradiation, air temperature and module voltage, while the outputs are the current and power generated by the panel. Analytical models of PV modules, based on the manufacturer datasheet values, are simulated through Matlab/Simulink environment. Multilayer perceptron is used to predict the operating current and power of the PV module. The best network configuration to predict panel current had a 3-7-4-1 topology. So, this two hidden layer topology was selected as the best model for predicting panel current with similar conditions. Results obtained from the PV module simulation and the optimal ANN model has been validated experimentally. Results showed that ANN model provide a better prediction of the current and power of the PV module than the analytical models. The coefficient of determination (R~2), mean square error (MSE) and the mean absolute percentage error (MAPE) values for the optimal ANN model were 0.971, 0.002 and 0.107, respectively. A comparative study among ANN and analytical models was also carried out. Among the analytical models, the five-parameter model, with MAPE = 0.112, MSE = 0.0026 and R~2 = 0.919, gave better prediction than the four-parameter model (with MAPE = 0.152, MSE = 0.0052 and R~2 = 0.905). Overall, the 3-7-4-1 ANN model outperformed four-parameter model, and was marginally better than the five-parameter model.
机译：在本文中，采用人工神经网络（ANN）来预测实际天气条件下的光伏（PV）面板行为。将人工神经网络的结果与光伏模块的解析四参数模型和五参数模型进行比较。模型的输入为每日总辐射量，空气温度和模块电压，而输出为面板产生的电流和功率。通过制造商数据表中的值，通过Matlab / Simulink环境模拟了光伏模块的分析模型。多层感知器用于预测PV模块的工作电流和功率。预测面板电流的最佳网络配置为3-7-4-1。因此，选择这两个隐藏层拓扑作为在相似条件下预测面板电流的最佳模型。从光伏组件仿真和最佳ANN模型获得的结果已通过实验验证。结果表明，与分析模型相比，人工神经网络模型可以更好地预测光伏组件的电流和功率。最优ANN模型的确定系数（R〜2），均方误差（MSE）和平均绝对百分比误差（MAPE）值分别为0.971、0.002和0.107。人工神经网络和分析模型之间也进行了比较研究。在分析模型中，MAPE = 0.112，MSE = 0.0026和R〜2 = 0.919的五参数模型比四参数模型（MAPE = 0.152，MSE = 0.0052和R〜2 = 0.905的四参数模型提供了更好的预测）。总体而言，3-7-4-1 ANN模型优于四参数模型，并且略好于五参数模型。

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