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Research on data-driven modeling for photovoltaic characteristics based on hybrid neural network
- Chinese Journal of Intelligent Science and Technology Vol. 2, Issue 2, Pages: 169-178(2020)
- 作者机构：
- 作者简介：
- 基金信息：
DOI：10.11959/j.issn.2096-6652.202019
CLC： TP183-
Published Online：2020-06，
Published：20 June 2020
- 稿件说明：
移动端阅览
Guobin ZHANG, Xinying WANG. Research on data-driven modeling for photovoltaic characteristics based on hybrid neural network[J]. Chinese journal of intelligent science and technology, 2020, 2(2): 169-178.
Guobin ZHANG, Xinying WANG. Research on data-driven modeling for photovoltaic characteristics based on hybrid neural network[J]. Chinese journal of intelligent science and technology, 2020, 2(2): 169-178. DOI： 10.11959/j.issn.2096-6652.202019.
摘要
针对传统物理机理建模方法不适用于复杂光照条件下光伏组件建模的问题，提出一种基于混合结构神经网络的光伏组件输出特性数据驱动建模方法。在深入分析光伏组件物理机理及输出特性的基础上，提出利用卷积神经网络和径向基函数神经网络对不均匀光照条件、温度、湿度等环境因素进行特征提取，并对光伏组件的输出特性进行仿真拟合。为提高模型的拟合效果，提出针对不均匀光照条件的阴影形态等效分析方法，同时采用改进型的遗传编码方案对网络参数进行优化，最后利用实际运行数据对模型效果进行分析验证。结果表明，该模型对不均匀光照条件具有一定的泛化跟踪能力，同时仿真结果平均误差保持在7%以内。
Abstract
A data-driven modeling approach of photovoltaic modules based on hybrid structure neural network was proposed to solve the issue that traditional physical mechanism modeling methods are difficult to be applied under some complex lighting conditions.Based on an in-depth analysis of the physical mechanism and output characteristics of photovoltaic modules
the convolutional neural network and radial basis function network were used to extract features of several environmental factors such as uneven lighting conditions
temperature and humidity.In order to improve the fitting effect of the hybrid network model
an equivalent analysis method of shadow morphology for uneven lighting conditions was proposed
a genetic coding scheme was used to optimize network parameters.Model effect was verified by the actual operation data.The model had generalized tracking capacity under various uneven lighting conditions
and the average error of simulation results was kept within 7%.
关键词
Keywords
references
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