基于云图特征自识别的光伏超短期预测模型  

Ultra-short-term Prediction of Self-identifying Photovoltaic Based on Sky Cloud Chart

作　　者：柴闵康 夏飞 张浩[2] 陆剑峰[2] 崔承刚 马波 CHAI Minkang;XIA Fei;ZHANG Hao;LU Jianfeng;CUI Chenggang;MA Bo(College of Automation Engineering,Shanghai University of Electric Power,Yangpu District,Shanghai 200090,China;College of Electronic and Information Engineering,Tongji University,Jiading District,Shanghai 201804,China)

机构地区：[1]上海电力大学自动化工程学院,上海市杨浦区200090 [2]同济大学电子与信息工程学院,上海市嘉定区201804

出　　处：《电网技术》2021年第3期1023-1031,共9页Power System Technology

基　　金：上海市2019年度“科技创新行动计划”社会发展科技领域项目(No.19DZ1206800)。

摘　　要：光伏电池超短期输出功率变化的主要原因来源于云层的无规则运动,会在1~2min的时间尺度内显著地影响输出功率,因此提出了天空云图预测方法提高光伏超短期功率预测的准确性。首先,采用云层灰度鉴别对云图提取云形状、云透射率等信息。然后,通过云点跟踪对云运动进行还原,得到云层的位移和速度等信息。接下来提出了云图特征联想和长短期记忆(cloudfeatureassociation-longshort-term memory,CFA-LSTM)模型,通过在LSTM模型中加入图像特征联想(cloud feature association,CFA),从而将光伏超短期输出功率与天空云图关联起来。最后基于云增强现象(cloud enhancement model,CEM),提出了由晴空辐照度作为标准的CEM-LSTM切换模型。实验证明,CEM-LSTM切换模型在全气候条件下不仅可以满足光伏超短期功率预测高精度的准确性需求,还可以满足光伏超短期功率预测高精度、高稳定性的可靠性需求,为光伏电站的高效经济运行提供了可能。The main reason that the ultra-short-term output power of photovoltaic cells changes comes from the random movement of clouds,which will significantly affect the output power within a time scale of 1-2 minutes.Therefore,a sky cloud chart prediction is proposed to improve the accuracy of the ultra-short-term power prediction of photovoltaics.Firstly,the cloud shape gray discrimination is used to extract the cloud shape,the cloud transmittance and other information from the cloud images.Then,the cloud motion is restored through the cloud point tracking to obtain information such as the cloud layer displacement and its velocity.Next,the CFA-LSTM model is proposed.By adding the cloud feature association to the LSTM model,the ultra-short-term photovoltaic output power is associated with the sky cloud images.Finally,based on the cloud enhancement,a CEM-LSTM switching model with clear sky irradiance as the standard is proposed.Experiments show that the CEM-LSTM switching model can not only meet the accuracy requirements of the photovoltaic ultra-short-term power prediction with high accuracy under all weather conditions.It can also meet the reliability requirements of high precision and high stability of PV ultra-short-term power prediction,which provides the possibility of efficient and economical operation of photovoltaic power plants.

关 键 词：云图特征自识别 云层鉴别 云点跟踪 切换模型 云图特征联想和长短期记忆 

分 类 号：TM721[电气工程—电力系统及自动化]