Data-Driven Modeling of a Commercial Photovoltaic Microinverter
Data-Driven Modeling of a Commercial Photovoltaic Microinverter
复制标题
商业光伏微型逆变器的数据驱动建模
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
A. Benigni
中科院分区：
文献类型：
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作者：
Hayder D. Abbood;A. Benigni
我们提出了一种用于商用光伏（PV）微逆变器静态建模的数据驱动建模（DDM）方法。所提出的建模方法处理所有可能的微逆变器工作模式，包括突发模式。在开发模型时，不需要预先了解内部组件、结构和控制算法。该方法基于人工神经网络（ANN）和快速傅里叶变换（FFT）。为了生成用于训练模型的数据，采用了电源硬件在环（PHIL）方法。从商用光伏微型逆变器的终端在时域上采集瞬时输入输出数据。然后，利用快速傅里叶变换（FFT）将采集到的数据转换到频域。作为DDM核心的人工神经网络是在频域开发的。然后将人工神经网络的输出转换回时域以进行验证并用于系统级仿真。实测数据和仿真数据的对比验证了所提方法的有效性。
We present a data-driven modeling (DDM) approach for static modeling of commercial photovoltaic (PV) microinverters. The proposed modeling approach handles all possible microinverter operating modes, including burst mode. No prior knowledge of internal components, structure, and control algorithm is assumed in developing the model. The approach is based on Artificial Neural Network (ANN) and Fast Fourier Transform (FFT). To generate the data used to train the model, a Power Hardware in the Loop (PHIL) approach is applied. Instantaneous inputs-outputs data are collected from the terminals of a commercial PV microinverter at time domain. Then, the collected data are converted to the frequency domain using Fast Fourier Transform (FFT). The ANNs that are the core of the DDM are developed in frequency domain. The outputs of the ANNs are then converted back to time domain for validation and use in system level simulation. The comparison between measured and simulated data validates the performance of the presented approach.