基于高阶小波神经网络的光伏发电系统表征

2014 World Automation Congress (WAC) Pub Date : 2014-10-27 DOI:10.1109/WAC.2014.6936021

L. J. Ricalde, E. H. Rubio, E. Ordonez, Lifter O. Ricalde

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引用次数: 1

摘要

本文主要研究了神经网络在光伏阵列预测中的应用。采用扩展卡尔曼滤波训练算法训练高阶小波神经网络，实现了智能电网的数据建模。回归向量的长度使用Cao方法确定。利用光伏组件的真实数据值进行仿真，说明了该体系结构的适用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Characterization for photovoltaic generation systems via Higher Order Wavelet Neural Networks

This paper focusses on applications of neural networks for forecasting in photovoltaic arrays. A Higher Order Wavelet Neural Network trained with an extended Kalman Filter training algorithm is implemented for data modeling in smart grids. The length of the regression vector is determined using the Cao methodology. The applicability of this architecture is illustrated via simulation using real data values from Photovoltaic modules.

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2014 World Automation Congress (WAC)

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