考虑风力波动相关性的风电场超短期出力预测

李传栋; 张明慧; 张逸; 弋子渊; 牛华清

doi:doi:10.19912/j.0254-0096.tynxb.2024-1299

English Version Reader LoginJournal Login

您当前的位置：

首页 >

文章列表页 >

考虑风力波动相关性的风电场超短期出力预测

更新时间：2025-12-10

- 考虑风力波动相关性的风电场超短期出力预测
- Issue 11, Pages: 754-763(2025)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1299
  CLC： M614
- Published Online：04 December 2025，
  
  Published：04 December 2025
- 稿件说明：
移动端阅览
李传栋, 张明慧, 张逸, et al. 考虑风力波动相关性的风电场超短期出力预测[J]. 2025, (11): 754-763.
DOI：

李传栋, 张明慧, 张逸, et al. 考虑风力波动相关性的风电场超短期出力预测[J]. 2025, (11): 754-763. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1299.

摘要

为提升风速波动下风电场超短期出力预测的准确性

提出一种考虑邻近风电场间风力波动时空关联性的超短期出力预测新方法。首先基于风速、风向和风电场间相对位置

计算出力波动间的时间差

并以此为依据确定有先验信息预测时段;其次运用变分贝叶斯模型

提取邻近风电场出力波动对待测出力影响的隐式关系

实现有先验信息时段出力的预测;最后

补全无先验信息时段预测得到完整的超短期预测周期的出力预测。以福建省3座风电场的实测数据进行验证

结果表明所提方法能有效利用邻近风电场的出力波动特征

提高目标风电场超短期出力预测的准确率。

Abstract

In order to improve the accuracy of ultra-short-term output prediction of wind farms under wind speed fluctuations

this paper proposes a new method for ultra-short-term output prediction considering the spatial and temporal correlation of wind fluctuations between adjacent wind farms. Firstly

based on the relative position of wind speed

wind direction and the relative position of wind farms

the time difference between output fluctuations is calculated

and the prediction time period with prior information is determined based on this basis. Secondly

the variational Bayesian model is used to extract the implicit relationship between the output fluctuation of the adjacent wind farm and the influence of the measured output

and the prediction of the output of the prior information period is realized. Finally

the output prediction of the complete ultra-short-term prediction cycle is obtained by completing the period prediction without prior information. The measured data of three wind farms in Fujian Province are used for verification. The results show that the proposed method can effectively utilize the output fluctuation characteristics of adjacent wind farms and improve the accuracy of ultra-short-term output prediction of target wind farms.

关键词

Keywords

references

刘其辉, 吴勇, 闫佳颖, 等. 考虑相位跳变的双馈风电机组低电压穿越特性分析与暂态过电压抑制[J]. 太阳能学报, 2024, 45(5): 86-94.

VERMA N, KUMAR N, KUMAR R.Battery energy storage-based system damping controller for alleviating sub-synchronous oscillations in a DFIG-based wind power plant[J]. Protection and control of modern power systems, 2023, 8(2): 1-18.

陈海鹏, 李赫, 阚天洋, 等. 考虑风电时序特性的深度小波-时序卷积网络超短期风功率预测[J]. 电网技术, 2023, 47(4): 1653-1665.

任东方, 马家庆, 何志琴, 等. 基于AVMD-CNN-GRU-Attention的超短期风功率预测研究[J]. 太阳能学报, 2024, 45(6): 436-443.

彭晨宇, 陈宁, 高丙团. 结合多重聚类和分层聚类的超短期风电功率预测方法[J]. 电力系统自动化, 2020, 44(2): 173-180.

MEDINA S V, AJENJO U P.Performance improvement of artificial neural network model in short-term forecasting of wind farm power output[J]. Journal of modern power systems and clean energy, 2020, 8(3): 484-490.

XU J J, XIAO Z N, LIN Z H, et al.System bias correction of short-term hub-height wind forecasts using the Kalman filter[J]. Protection and control of modern power systems, 2021, 6: 37.

黎静华, 文劲宇, 程时杰, 等. 考虑多风电场出力Copula相关关系的场景生成方法[J]. 中国电机工程学报, 2013, 33(16): 30-36, 21.

赵书强, 金天然, 李志伟, 等. 考虑时空相关性的多风电场出力场景生成方法[J]. 电网技术, 2019, 43(11): 3997-4004.

赵永宁, 李卓, 叶林, 等. 基于时空相关性的风电功率超短期自适应预测方法[J]. 电力系统保护与控制, 2023, 51(6): 94-105.

兰飞, 农植贵, 黎静华. 风电功率序列的时空相关性研究[J]. 电力系统及其自动化学报, 2016, 28(1): 24-31.

廖雪超, 陈才圣, 伍杰平. 基于CNN-LSTM及深度学习的风电场时空组合预测模型[J]. 信息与控制, 2022, 51(4): 498-512.

李丹, 甘月琳, 缪书唯, 等. 计及时间演变和空间相关的多风电场短期功率预测[J]. 电网技术, 2023, 47(3): 1117-1128.

朱乔木, 李弘毅, 王子琪, 等. 基于长短期记忆网络的风电场发电功率超短期预测[J]. 电网技术, 2017, 41(12): 3797-3802.

叶林, 赵金龙, 路朋, 等. 考虑气象特征与波动过程关联的短期风电功率组合预测[J]. 电力系统自动化, 2021, 45(4): 54-62.

崔嘉, 杨俊友, 杨理践, 等. 基于改进CFD与小波混合神经网络组合的风电场功率预测方法[J]. 电网技术, 2017, 41(1): 79-85.

庞昊, 高金峰, 杜耀恒. 基于时间卷积网络分位数回归的短期负荷概率密度预测方法[J]. 电网技术, 2020, 44(4): 1343-1350.

沈忱. 贝叶斯网络模型的变分贝叶斯学习与推理研究[D]. 哈尔滨: 哈尔滨工程大学, 2015.

BLEI D M, KUCUKELBIR A, MCAULIFFE J D.Variational inference: a review for statisticians[J]. Journal of the American Statistical Association, 2017, 112(518): 859-877.

GAO S Q, ZHOU H Q, GAO Y B, et al.BayeSeg: Bayesian modeling for medical image segmentation with interpretable generalizability[J]. Medical image analysis, 2023, 89: 102889.

ZHANG Y, TIŇO P, LEONARDIS A, et al. A survey on neural network interpretability[J]. IEEE transactions on emerging topics in computational intelligence, 2021, 5(5): 726-742.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

ULTRA-SHORT-TERM PREDICTION METHOD OF DISTRIBUTED PHOTOVOLTAIC POWER OUTPUT BASED ON DIRECTED GRAPH CONVOLUTION RECURRENT NETWORK

Related Author

Zhao Hongshan

Sun Chengyan

Wen Kaiyun

Wu Yuchen

Related Institution

Hebei Key Laboratory of Distributed Energy Storage and Micro-grid （North China Electric Power University）

AI问答

⁰