基于混合神经网络的光伏组件输出特性数据驱动建模方法

张国宾; 王新迎

doi:10.11959/j.issn.2096-6652.202019

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基于混合神经网络的光伏组件输出特性数据驱动建模方法

学术论文 | 更新时间：2024-06-05

- 基于混合神经网络的光伏组件输出特性数据驱动建模方法
- Research on data-driven modeling for photovoltaic characteristics based on hybrid neural network
- 智能科学与技术学报 2020年2卷第2期页码：169-178
- 作者机构：
- 作者简介：
  
  [ "张国宾（1988- ），男，中国电力科学研究院有限公司工程师，主要研究方向为智能配电网及人工智能应用" ]
  [ "王新迎(1987- ），男，博士，中国电力科学研究院有限公司工程师，主要研究方向为能源互联网与人工智能应用" ]
- 基金信息：
  
  中国电力科学研究院有限公司创新基金项目;China Electric Power Research Institute Innovation Foundation Item(52420019000N)
- DOI：10.11959/j.issn.2096-6652.202019
  中图分类号： TP183
- 网络出版日期：2020-06，
  
  纸质出版日期：2020-06-20
- 稿件说明：
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张国宾, 王新迎. 基于混合神经网络的光伏组件输出特性数据驱动建模方法[J]. 智能科学与技术学报, 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.
张国宾, 王新迎. 基于混合神经网络的光伏组件输出特性数据驱动建模方法[J]. 智能科学与技术学报, 2020,2(2):169-178. DOI： 10.11959/j.issn.2096-6652.202019.

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%.

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Keywords

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