Short-term Power Forecasting Method of Wind Farm Based on Gaussian Mixture Model Clustering

Yimei WANG; Hui LIU; Peng SONG; Zechun HU; Linlin WU

doi:10.7500/AEPS20200616005

您当前的位置：

首页 >

文章列表页 >

Short-term Power Forecasting Method of Wind Farm Based on Gaussian Mixture Model Clustering

Basic Research | 更新时间：2025-06-16

- Short-term Power Forecasting Method of Wind Farm Based on Gaussian Mixture Model Clustering
- Automation of Electric Power Systems Vol. 45, Issue 7, Pages: 37-43(2021)
- 作者机构：
  
  1.国网冀北电力有限公司电力科学研究院，北京市 100045
  2.风光储并网运行技术国家电网公司重点实验室，北京市 100045
  3.清华大学电机工程与应用电子技术系，北京市 100084
- 作者简介：
- 基金信息：
  
  National Key R&D Program of China
- DOI：10.7500/AEPS20200616005
  CLC：
- Received：16 June 2020，
  
  Revised：2020-11-12，
  
  Published Online：18 February 2021，
  
  Published：10 April 2021
- 稿件说明：
移动端阅览
王一妹,刘辉,宋鹏等.基于高斯混合模型聚类的风电场短期功率预测方法[J].电力系统自动化,2021,45(7):37-43.

WANG Yimei,LIU Hui,SONG Peng,et al.Short-term Power Forecasting Method of Wind Farm Based on Gaussian Mixture Model Clustering[J].Automation of Electric Power Systems,2021,45(7):37-43.
王一妹,刘辉,宋鹏等.基于高斯混合模型聚类的风电场短期功率预测方法[J].电力系统自动化,2021,45(7):37-43. DOI： 10.7500/AEPS20200616005.

WANG Yimei,LIU Hui,SONG Peng,et al.Short-term Power Forecasting Method of Wind Farm Based on Gaussian Mixture Model Clustering[J].Automation of Electric Power Systems,2021,45(7):37-43. DOI： 10.7500/AEPS20200616005.

摘要

对任意来流条件下的风电场发电功率进行准确预测，是提高电网对风电接纳能力的有效措施。针对大型风电场的功率预测采用单点位风速外推预测代表性差的局限，提出基于高斯混合模型（GMM）聚类的风电场短期功率预测方法。方法结合数据分布特征，利用GMM聚类将大型风电场划分为若干机组群，借助贝叶斯信息准则指标评价，获得风电场内最优机组分组方案。实际算例验证表明，按照小时级、月度级、年度级等时间尺度进行统计，所建立的GMM聚类模型均极大地提高了未分组的风电功率预测模型的准确性。相较于应用广泛的

-means聚类、层次凝聚聚类等方法，GMM聚类方法在分组功率预测中表现出了显著优势，为大型风电场短期功率预测模型的优化及运行经济性的提升提供了技术支持与依据。

Abstract

Accurate forecasting of wind farm power under arbitrary inflow conditions is an effective way to improve the ability of the power grid to accept wind power. Aiming at the limitation of poor representativeness of single-point wind speed extrapolation for power forecasting of large-scale wind farms

a short-term power forecasting method of wind farm based on Gaussian mixture model (GMM) clustering is proposed. The method combines the characteristics of data distribution

uses GMM clustering to divide large-scale wind farms into several unit groups

and obtains the optimal unit grouping scheme in the wind farm based on the index evaluation of Bayesian information criterion. The actual example verifies that the established GMM clustering model greatly improves the accuracy of ungrouped wind power forecasting models based on hourly

monthly

annual and other time scales. Compared with widely used methods such as

-means clustering and hierarchical agglomerative clustering

the GMM clustering method shows significant advantages in grouped power forecasting

which provides technical support and basis for the optimization of short-term power forecasting models for large-scale wind farms and the improvement of the operation economy.

关键词

Keywords

references

REN Y ， SUGANTHAN P ， SRIKANTH N . Ensemble methods for wind and solar power forecasting： a state-of-the-art review ［J］. Renewable and Sustainable Energy Reviews ， 2015 ， 50 ： 82 - 91 .

阎洁，许成志，刘永前，等 . 基于风速云模型相似日的短期风电功率预测方法［J］. 电力系统自动化， 2018 ， 42 （ 6 ）： 53 - 59 .

YAN Jie ， XU Chengzhi ， LIU Yongqian ， et al . Short-term wind power prediction method based on wind speed cloud model in similar day ［J］. Automation of Electric Power Systems ， 2018 ， 42 （ 6 ）： 53 - 59 .

FANG S ， CHIANG H . A high-accuracy wind power forecasting model ［J］. IEEE Transactions on Power Systems ， 2016 ， 32 （ 2 ）： 1589 - 1590 .

丁明，张超，王勃，等 . 基于功率波动过程的风电功率短期预测及误差修正［J］. 电力系统自动化， 2019 ， 43 （ 3 ）： 2 - 9 .

DING Ming ， ZHANG Chao ， WANG Bo ， et al . Short-term forecasting and error correction of wind power based on power fluctuation process ［J］. Automation of Electric Power Systems ， 2019 ， 43 （ 3 ）： 2 - 9 .

LANGE M ， FOCKEN U . Physical approach to short-term wind power prediction ［M］. Berlin， Germany ： Springer ， 2006 .

WANG Yimei ， LIU Yongqian ， LI Li ， et al . Short-term wind power forecasting based on clustering pre-calculated CFD method ［J］. Energies ， 2018 ， 11 （ 4 ）： 854 .

牛东晓，纪会争 . 风电功率物理预测模型引入误差量化分析方法［J］. 电力系统自动化， 2020 ， 44 （ 8 ）： 57 - 65 .

NIU Dongxiao ， JI Huizheng . Quantitative analysis method for errors introduced by physical prediction model of wind power ［J］. Automation of Electric Power Systems ， 2020 ， 44 （ 8 ）： 57 - 65 .

薛禹胜，郁琛，赵俊华，等 . 关于短期及超短期风电功率预测的评述［J］. 电力系统自动化， 2015 ， 39 （ 6 ）： 141 - 151 .

XUE Yusheng ， YU Chen ， ZHAO Junhua ， et al . A review on short-term and ultra-short-term wind power prediction ［J］. Automation of Electric Power Systems ， 2015 ， 39 （ 6 ）： 141 - 151 .

高小力 . 大型风电场分组建模方法及其在功率预测中的应用［D］. 北京：华北电力大学， 2015 .

GAO Xiaoli . Clustering methods of wind turbines in large-scale wind farm and its application in wind power forecasts ［D］. Beijing ： North China Electric Power University ， 2015 .

WU W ， PENG M . A data mining approach combining k -means clustering with bagging neural network for short-term wind power forecasting ［J］. IEEE Internet of Things Journal ， 2017 ， 4 （ 4 ）： 979 - 986 .

WANG G ， JIA R ， LIU J ， et al . A hybrid wind power forecasting approach based on Bayesian model averaging and ensemble learning ［J］. Renewable Energy ， 2020 ， 145 ： 2426 - 2434 .

LIU Y ， GAO X ， YAN J ， et al . Clustering methods of wind turbines and its application in short-term wind power forecasts ［J］. Journal of Renewable and Sustainable Energy ， 2014 ， 6 （ 5 ）： 474 - 482 .

NOWAKOWSKA E ， KORONACKI J ， LIPOVETSKY S . Clusterability assessment for Gaussian mixture models ［J］. Applied Mathematics and Computation ， 2015 ， 256 ： 591 - 601 .

YANG M ， LAI C ， LIN C . A robust EM clustering algorithm for Gaussian mixture models ［J］. Pattern Recognition ， 2012 ， 45 （ 11 ）： 3950 - 3961 .

LORAH J ， WOMACK A . Value of sample size for computation of the Bayesian information criterion （BIC） in multilevel modeling ［J］. Behavior research methods ， 2019 ， 51 （ 1 ）： 440 - 450 .

LIN Zhenzhi ， WEN Fushuan ， DING Yi ， et al . Data-driven coherency identification for generators based on spectral clustering ［J］. IEEE Transactions on Industrial Informatics ， 2018 ， 14 （ 3 ）： 1275 - 1285 .

SIDERATOS G ， HATZIARGYRIOU N D . Probabilistic wind power forecasting using radial basis function neural networks ［J］. IEEE Transactions on Power Systems ， 2012 ， 27 （ 4 ）： 1788 - 1796 .

王育飞，付玉超，孙路，等 . 基于混沌-RBF神经网络的光伏发电功率超短期预测模型［J］. 电网技术， 2018 ， 42 （ 4 ）： 1110 - 1116 .

WANG Yufei ， FU Yuchao ， SUN Lu ， et al . Ultra-short term prediction model of photovoltaic output power based on chaos-RBF neural network ［J］. Power System Technology ， 2018 ， 42 （ 4 ）： 1110 - 1116 .

SINAGA K P ， YANG M S . Unsupervised k -means clustering algorithm ［J］. IEEE Access ， 2020 ， 8 ： 80716 - 80727 .

王尤嘉，鲁宗相，乔颖，等 . 基于特征聚类的区域风电短期功率统计升尺度预测［J］. 电网技术， 2017 ， 41 （ 5 ）： 1383 - 1389 .

WANG Youjia ， LU Zongxiang ， QIAO Ying ， et al . Short-term regional wind power statistical upscaling forecasting based on feature clustering ［J］. Power System Technology ， 2017 ， 41 （ 5 ）： 1383 - 1389 .

刘小瑞 . 基于数据驱动的风电场功率预测及其在源荷协调中的应用［D］. 湘潭：湘潭大学， 2018 .

LIU Xiaorui . Data-driven wind farm power forecasting and its application in source-load coordination ［D］. Xiangtan ： Xiangtan University ， 2018 .

HAN Pingping ， XIA Yu ， ZHANG Yan ， et al . Equivalent model of wind farm based on DBSCAN ［C］// 2017 IEEE Innovative Smart Grid Technologies-Asia （ISGT-Asia）， December 4 - 7 ， 2017 ， Auckland ， New Zealand .

CHAUDHARY A ， SHARMA A ， KUMAR A ， et al . Short term wind power forecasting using machine learning techniques ［J］. Journal of Statistics and Management Systems ， 2020 ， 23 （ 1 ）： 145 - 156 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Feedback Control Strategy for Wind Power Auxiliary Frequency Regulation Based on System Frequency Trajectory Optimization

Switched System Modeling for Repeated Low Voltage Ride-through of Wind Turbine and Mechanism Analysis of Voltage Oscillations

Variable-voltage Operation Control Method for Wind Power System with Flexible Low-frequency Transmission

System Frequency Response Model of Deloaded Wind Power System Considering Working Point Shifting

Power Forecasting for Photovoltaic Cluster in Long Forecasting Period Based on Power Reconstruction and Temporal Characteristic Constraints

Related Author

GAO Yingjie

XU Yin

WU Xiangyu

CHANG Zhengwei

LAI Qiping

SHEN Chen

YANG Yanchen

LI Dongsheng

Related Institution

State Grid Sichuan Electric Power Company

School of Electrical Engineering, Beijing Jiaotong University

State Grid Jibei Electric Power Research Institute

Engineering Research Center of Offshore Wind Technology, Ministry of Education (Shanghai University of Electric Power)

Electric Power Research Institute of State Grid Shanghai Municipal Electric Power Company

⁰