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Modeling the output power of heterogeneous photovoltaic panels based on artificial neural networks using low cost microcontrollers

机译：使用低成本微控制器基于人工神经网络对异构光伏面板的输出功率建模

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摘要

Many implementations of artificial neural networks have been reported in scientific papers. However, few of these implementations allow the direct use of off-line trained networks. Moreover, no implementation reported the use of relatively small network adequate to run on low cost microcontroller. Hence, this work, which presents a small artificial neural network, which models the output power of heterogeneous photovoltaic panel. In addition, the work discuss the hardware implementation that allows such network to run on low cost microcontroller. The hardware implementation has the ability to model heterogeneous photovoltaic panel's output power with very high accuracy and fast response time. Feedforward back propagation has been used because of its high resolution and accurate activation function. Real-time measured parameters can be used as inputs for the developed system. The resulting hardware data is tested with data from real photovoltaic panels; to confirm that it can efficiently implement the models prepared off-line with Matlab. The comparison revealed the robustness of the proposed heterogeneous photovoltaic model system at different conditions. The proposed heterogeneous photovoltaic model system offer a proper and efficient tool that can be used in monitoring photovoltaic panels, such as the ones used in smart-house applications.
机译：人工神经网络的许多实现已在科学论文中报道。但是，这些实现中很少有允许直接使用脱机训练的网络。此外，没有实现报告使用相对较小的网络足以在低成本微控制器上运行。因此，这项工作提出了一个小型的人工神经网络，该网络可以模拟异构光伏面板的输出功率。此外，本文讨论了允许这种网络在低成本微控制器上运行的硬件实现。硬件实现能够以非常高的精度和快速的响应时间对异构光伏面板的输出功率进行建模。由于其高分辨率和精确的激活功能，因此使用了前馈反向传播。实时测量的参数可用作已开发系统的输入。使用来自实际光伏面板的数据测试所得的硬件数据；确认它可以有效地实现通过Matlab离线准备的模型。比较显示了所提出的异构光伏模型系统在不同条件下的鲁棒性。所提出的异构光伏模型系统提供了一种合适且有效的工具，可用于监视光伏面板，例如智能住宅应用中使用的面板。

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