一种基于神经网络的天空图像到太阳辐照度的映射模型  

Mapping model of sky image to solar irradiance based on neural network

作　　者：林琳 刘譞[1] 康慧玲 Lin Lin;Liu Xuan;Kang Huiling(Baoding Electric Power Vocational and Technical College(State Grid Jibei Electric Power Company Limited Skills Training Center),Baoding 071051,China)

机构地区：[1]保定电力职业技术学院(国网冀北电力有限公司技能培训中心),保定071051

出　　处：《电子测量技术》2020年第14期74-78,共5页Electronic Measurement Technology

摘　　要：由于太阳辐射的随机波动性,大型光伏并网发电会给电力系统的稳定运行带来严重影响。为了实现基于天空图像的超短期光伏功率预测,提高电网对光伏功率的消纳能力,一个能够将天空图像映射到相应地表太阳辐射的精确模型具有重要意义。因此,提出了一种基于神经网络的天空图像到太阳辐照度的映射模型。首先,结合地外太阳辐照度和大气光学厚度理论计算值,建立净空表面辐照度模型;其次,对全天空成像仪观测到的天空图像进行处理,提取与太阳辐照度相关的图像特征;最后,利用历史天空图像和太阳辐照度数据,建立基于神经网络的辐照度模型。仿真结果表明,该模型能够准确地将不同天气条件下的天空图像特征映射到地表太阳辐照度,验证了该模型的有效性和实用性。Due to the stochastic fluctuant characteristic of solar irradiance,large-scale grid-connected photovoltaic(PV)power plant can bring great difficulties to the operation of power system.In order to fulfil the sky images based ultra-short term PV power forecasting and enhance the grid consumptive ability of PV power,an accurate model that can mapping sky images to corresponding surface solar irradiance is very significant.Therefore,in this paper a neural network based irradiance mapping model of solar PV power forecasting using sky image is proposed.First,we combine the theoretical calculation of extraterrestrial solar irradiance and atmospheric optical thickness to establish the clearance surface irradiance model.Second,the sky images observed by total sky imager are processed to extract image features related to solar irradiance.Third,a neural network based irradiance mapping model is built and trained using historical sky images and solar irradiance data.Simulation results show that the proposed model can map sky image features to surface solar irradiance accurately in different weather conditions.

关 键 词：光伏发电预测 辐照度映射模型 天空图像 神经网络 

分 类 号：TM73[电气工程—电力系统及自动化]