融合精细化气象因素与物理约束的深度学习模型在短期风电功率预测中的应用  

Application of Deep Learning Model Integrating Refined Meteorological Factors and Physical Constraints in Short-term Wind Power Prediction

作　　者：邬永 王冰[1] 陈玉全 姜华 WU Yong;WANG Bing;CHEN Yuquan;JIANG Hua(School of Energy and Electrical Engineering,Hohai University,Nanjing 211100,Jiangsu Province,China)

机构地区：[1]河海大学能源与电气学院,江苏省南京市211100

出　　处：《电网技术》2024年第4期1455-1465,I0022,I0023,I0024,共14页Power System Technology

基　　金：国家自然科学基金项目(62303158)。

摘　　要：现有基于深度学习方法的风电功率预测是一种以气象数据为输入的间接预测,其预测精度依赖于气象预报的准确率,然而现有气象预报资料普遍存在分辨率低,预报模式不稳定的问题。同时,深度学习模型完全依赖数据驱动,缺乏物理规律的指导,预测精度难以进一步提升。因此,提出一种精细化气象因素与物理深度学习相结合的方法。首先,通过降尺度与多模式集成技术,对数值天气预报数据进行处理,改善气象预报产品的低分辨率和准确率问题;其次,基于风电场尾流效应和功率曲线两种物理模型,一方面将物理模型嵌入神经网络损失函数作为正则化项,引入物理约束指导学习过程,以构建物理深度学习网络;另一方面,利用物理模型产生预训练样本,解决观测数据不足的情况,构建预训练模型,为后续有监督学习任务提供支持。最后,通过对某市近海风电场的实际数据进行仿真分析,验证了所提出方法的有效性和优越性。Existing wind power prediction based on deep learning methods is an indirect prediction using meteorological data as input,and its prediction accuracy depends on the accuracy of meteorological forecasts.However,existing meteorological forecast data generally suffer from low-resolution and unstable models.At the same time,deep learning models are completely data-driven and need guidance from physical laws,making it difficult to improve prediction accuracy further.Therefore,this paper proposes a method that combines refined meteorological factors with physical deep learning.Firstly,numerical weather prediction data is processed using downscaling and multi-model integration techniques to improve meteorological forecast products'low resolution and accuracy issues.Secondly,two physical models are introduced based on wind turbine wake effects and power curves.On the one hand,the physical models are embedded in the neural network loss function as regularization terms to introduce physical constraints in the learning process and construct a physical deep learning network.On the other hand,the physical models are used to generate pre-training samples to address the insufficient observational data,obtaining a pre-training model that supports subsequent supervised learning tasks.Finally,the effectiveness and superiority of the proposed method are verified through simulation analysis of actual data from a coastal wind farm in a certain city.

关 键 词：风电功率 数值天气预报 降尺度 多模式集成 物理深度学习 

分 类 号：TM614[电气工程—电力系统及自动化]