计及尾流效应的海上风电场超短期功率预测

魏书荣; 彭冉晟; 符杨; 杨心刚; 方陈

doi:doi:10.19912/j.0254-0096.tynxb.2024-2142

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

计及尾流效应的海上风电场超短期功率预测

更新时间：2026-07-22

- 计及尾流效应的海上风电场超短期功率预测
- Vol. 47, Issue 4, Pages: 268-277(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-2142
  CLC： TM614
- Online First：08 May 2026，
  
  Published：2026
- 稿件说明：
移动端阅览
魏书荣, 彭冉晟, 符杨, et al. 计及尾流效应的海上风电场超短期功率预测[J]. 2026, 47(4): 268-277.
DOI：

魏书荣, 彭冉晟, 符杨, et al. 计及尾流效应的海上风电场超短期功率预测[J]. 2026, 47(4): 268-277. DOI： doi:10.19912/j.0254-0096.tynxb.2024-2142.

摘要

由于海上风电具有低海平面粗糙度、风力机布局密集等特点

其发电功率受尾流影响显著

特提出一种计及尾流效应的海上风电场超短期功率预测方法。首先

采用天气研究与预报模式（WRF）耦合风电场参数化模型（WFP）量化评估相邻海上风电场间的尾流效应

将所得尾流量化指标作为预测模型的输入数据;然后

在考虑尾流影响的基础上

建立基于可学习邻接矩阵的时空图卷积网络、空间自适应特征调制模块（SAFM）和部分卷积网络（PConv）的预测模型;最后

将所提预测方法应用于江苏某相邻海上风电场群

量化分析风电场间尾流效应。算例表明

所提方法可显著提升海上风电场功率预测精度。

Abstract

Accurate prediction of ultra-short-term power of offshore wind farm is an important means to ensure the safe operation of offshore wind power system. Because offshore wind power has the characteristics of low sea level roughness and dense layout of wind turbines

its power generation is significantly affected by wake. An ultra-short-term power prediction method for offshore wind farms considering wake effect is proposed. Firstly

the weather research and forecasting model （WRF） coupled with the wind farm parameterization （WFP） is used to quantitatively evaluate the wake effect between adjacent offshore wind farms

and the obtained wake quantification index is used as the input data of the prediction model. Then

on the basis of considering wake effect

the prediction models of spatio-temporal graph convolutional network

spatially adaptive feature modulation module （SAFM） and partial convolutional （PConv） based on learnable adjacency matrix are established. Finally

the proposed prediction method is applied to an adjacent offshore wind farm group in Jiangsu Province to quantitatively analyze the wake effect between wind farms. The example shows that the proposed method can significantly improve the power prediction accuracy of offshore wind farms and offer technical assistance for the high-quality advancement of offshore wind power.

关键词

Keywords

references

郑崇伟. “21世纪海上丝绸之路”: 海上风能的宏观与微观等级区划[J]. 哈尔滨工程大学学报, 2024, 45(7): 1258-1265.

NYGAARD N G, NEWCOMBE A C.Wake behind an offshore wind farm observed with dual-Doppler radars[C]//7th Conference on Science of Making Torque from Wind. Milan, Italy, June 20-22, 2018.

SCHNEEMANN J, ROTT A, DÖRENKÄMPER M, et al. Cluster wakes impact on a far-distant offshore wind farm’s power[J]. Wind energy science, 2020, 5(1): 29-49.

符杨, 任子旭, 魏书荣, 等. 基于改进LSTM-TCN模型的海上风电超短期功率预测[J]. 中国电机工程学报, 2022, 42(12): 4292-4302.

GUO N Z, SHI K Z, LI B, et al.A physics-inspired neural network model for short-term wind power prediction considering wake effects[J]. Energy, 2022, 261: 125208.

ZHANG Z Y, HUANG P.Prediction of multiple-wake velocity and wind power using a cosine-shaped wake model[J]. Renewable energy, 2023, 219: 119418.

彭冉晟, 魏书荣. 基于DTW-DBSCAN和MTGNN-SA的海上风电集群功率预测[J]. 上海电力大学学报, 2025, 41(3): 223-230, 308.

向阳, 刘亚娟, 孙志伟, 等. 基于帝王蝶算法的CNN-GRU-LightGBM模型短期风电功率预测[J]. 太阳能学报, 2025, 46(1): 105-114.

傅质馨, 王宝池, 刘皓明, 等. 基于风速-功率组合分解重构的海上风电功率预测[J]. 太阳能学报, 2024, 45(11): 418-426.

杨子民, 彭小圣, 熊予涵, 等. 计及邻近风电场信息与CNN-BiLSTM的短期风电功率预测[J]. 南方电网技术, 2023, 17(2): 47-56.

孙亦皓, 刘浩, 胡天宇, 等. 基于时空关联特征与GCN-FEDformer的风速短期预测方法[J]. 中国电机工程学报, 2024, 44(21): 8496-8507.

龚钢军, 蔡贺, 杨德龙, 等. 基于改进时空图卷积网络的综合能源系统超短期联合预测方法[J]. 华北电力大学学报(自然科学版), 2025, 52(4): 1-12, 23, 13.

陈子含, 滕伟, 胥学峰, 等. 基于图卷积网络和风速差分拟合的中长期风功率预测[J]. 中国电力, 2023, 56(10): 96-105.

罗颖, 刘雨辰, 米立华, 等. 基于WRF模拟和注意力机制的短期风速预测[J]. 太阳能学报, 2023, 44(9): 302-310.

MUÑOZ-SABATER J, DUTRA E, AGUSTÍ-PANAREDA A, et al. ERA5-Land: a state-of-the-art global reanalysis dataset for land applications[J]. Earth system science data, 2021, 13(9): 4349-4383.

穆延非, 王强, 罗坤, 等. 基于中尺度WRF模式的海上风电场尾流影响评估[J]. 中国电机工程学报, 2022, 42(S1): 193-203.

FISCHEREIT J, VEDEL H, LARSÉN X G, et al. Modelling wind farm effects in HARMONIE-AROME (cycle 43.2.2)-Part 1: Implementation and evaluation[J]. Geoscientific model development, 2024, 17(7): 2855-2875.

PRYOR S C, BARTHELMIE R J.Wind shadows impact planning of large offshore wind farms[J]. Applied energy, 2024, 359: 122755.

房方, 张效宁, 姚贵山, 等. 基于中尺度气象模式分析的海陆风电场间尾流扰动影响评估[J]. 中国电机工程学报, 2022, 42(13): 4848-4859.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

基于信息熵聚类分解和CTA-BiLSTM的超短期风电功率预测

基于TCN-NCP-CFC神经网络的超短期风电功率预测

基于多头注意力机制的ResNet-UNet短期风电功率预测

基于VMD和多时间尺度分类预测的风电单机短期功率预测研究

深度强化学习优化非线性自抗扰微网稳压技术

Related Author

李天白

顾军华

秦玉龙

张素琪

赵飞

吴文标

汪燕毅

顾婷婷

Related Institution

Key Laboratory of Modern Power System Simulation Control and New Green Energy Technology of Ministry of Education （Northeast Electric Power University）

School of Control and Computer Engineering, North China Electric Power University

China Nuclear Power Engineering Co., Ltd. Zhengzhou Branch

Longyuan（Beijing）Wind Power Engineering Technology Co., Ltd.

College of Electrical Engineering, Guizhou University

AI问答

⁰