摘要
光伏电站数据为时间序列数据,会受到通信传输、逆变器采集等因素的影响而包含大量异常数据,故该文研究一种基于深度学习的光伏电站数据预处理算法,进行数据清洗等预处理。一方面,根据组串逆变器的工作特性,对光伏电站数据的常见异常类型进行分析标记,结合滑动窗口法划分数据,构建用于深度学习训练的光伏电站数据集。另一方面,从激活函数、损失函数以及隐藏层等方面优化GRU神经网络模型,并利用自建数据集对该模型进行训练和测试。测试结果表明:该模型在实际光伏电站数据上的处理准确率达99.84%。
Running data from the photovoltaic power system is time indexed,which may be incomplete caused by low quality communication, and always contain the amount of abnormal data from the inverter. This paper studies the algorithm to preprocess photovoltaic data before being used to evaluate the whole system performance. The preprocessing includes labeling abnormal data and cleaning noise data. One optimized GRU neural network is used to do that, which is trained on our lab-built dataset. The GRU network is optimized to process photovoltaic data more efficiently with the activation function, loss function, and hidden layer. The best accuracy is as good as 99.84% from the test dataset consisting of actual photovoltaic data which is not used in training.
作者
王丽朝
孟子尧
陈诗明
许盛之
龚友康
赵颖
Wang Lichao;Meng Ziyao;Chen Shiming;Xu Shengzhi;Gong Youkang;Zhao Ying(Institute of Photo-Electronics Thin Film Devices and Technology of College of Electronic Information and Optical Engineering of Nankai University,Tianjin 300350,China;Engineering Research Center of Thin Film Optoelectronics Technology,Ministry of Education,Tianjin 300350,China;Key Laboratory of Photoelectronics Thin Film Devices and Technology,Tianjin 300350,China;Big Data Management Center of Nankai University,Tianjin 300350,China)
出处
《太阳能学报》
EI
CAS
CSCD
北大核心
2022年第11期78-84,共7页
Acta Energiae Solaris Sinica
关键词
光伏发电
逆变器
神经网络
数据处理
网络性能
photovoltaic power generation
electric inverters
neural networks
data processing
network performance
