Ultra-short-term Wind Power Forecasting Method Combining Multiple Clustering and Hierarchical Clustering

Chenyu PENG; Ning CHEN; Bingtuan GAO

doi:10.7500/AEPS20190105003

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Ultra-short-term Wind Power Forecasting Method Combining Multiple Clustering and Hierarchical Clustering

Application Research | 更新时间：2025-06-16

- Ultra-short-term Wind Power Forecasting Method Combining Multiple Clustering and Hierarchical Clustering
- Automation of Electric Power Systems Vol. 44, Issue 2, Pages: 172-179(2020)
- 作者机构：
  
  1.东南大学电气工程学院,江苏省南京市 210096
  2.新能源与储能运行控制国家重点实验室,江苏省南京市 210003
  3.中国电力科学研究院有限公司(南京),江苏省南京市 210003
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China;State Grid Corporation of China
- DOI：10.7500/AEPS20190105003
  CLC：
- Received：05 January 2019，
  
  Revised：2019-04-14，
  
  Published Online：30 July 2019，
  
  Published：25 January 2020
- 稿件说明：
移动端阅览
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(02):172-179.

PENG Chenyu,CHEN Ning,GAO Bingtuan.Ultra-short-term Wind Power Forecasting Method Combining Multiple Clustering and Hierarchical Clustering[J].Automation of Electric Power Systems,2020,44(02):172-179.
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(02):172-179. DOI： 10.7500/AEPS20190105003.

PENG Chenyu,CHEN Ning,GAO Bingtuan.Ultra-short-term Wind Power Forecasting Method Combining Multiple Clustering and Hierarchical Clustering[J].Automation of Electric Power Systems,2020,44(02):172-179. DOI： 10.7500/AEPS20190105003.

摘要

提出了一种结合多重聚类算法和分层聚类算法的超短期风电功率预测方法。为了处理训练样本动态

识别与待预测时段特征相似的样本

对历史功率序列和历史气象序列分别进行聚类处理。功率序列的聚类指标由欧氏距离和协方差组成

气象序列的聚类采用逐层划分的方法

并将聚类结果组合成多个样本子集。利用分类建模-特征匹配的思路建立多个粒子群优化-反向传播(PSO-BP)神经网络预测模型

并调用与待预测时段特征最相似的预测模型。将所提预测方法用于青海某风电场的实际算例

实验结果表明

该方法可以提高超短期风电预测的准确性。

Abstract

An ultra-short-term wind power forecasting method combining multiple clustering algorithm and hierarchical clustering algorithm is proposed. To deal with the dynamic condition of training samples and identify the samples that are similar to the characteristics of the period to be predicted

the historical power series and historical meteorological series are clustered separately. The clustering index of the power series consists of Euclidean distance and covariance

and the layer-by-layer method is used for meteorological series clustering. Two clustering results are combined into multiple sample subsets. Multiple neural network forecasting models based on particle swarm optimization and back propagation (PSO-BP) are established by using the method of classification modeling and feature matching. And the model with the most similar characteristics to the predicted period are used. The proposed forecasting approach has been applied in actual wind generation data tracking in Qinghai province of China. The simulation results show that it can improve the forecasting accuracy of ultra-short-term wind power.

关键词

Keywords

references

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 .

钱政 , 裴岩 , 曹利宵 , 等 . 风电功率预测方法综述 [J]. 高电压技术 , 2016 , 42 ( 4 ): 1047 - 1060 .

QIAN Zheng , PEI Yan , CAO Lixiao , et al . Review of wind power forecasting method [J]. High Voltage Engineering , 2016 , 42 ( 4 ): 1047 - 1060 .

冯双磊 , 王伟胜 , 刘纯 , 等 . 风电场功率预测物理方法研究 [J]. 中国电机工程学报 , 2010 , 30 ( 2 ): 1 - 6 .

FENG Shuanglei , WANG Weisheng , LIU Chun , et al . Study on the physical approach to wind power prediction [J]. Proceedings of the CSEE , 2010 , 30 ( 2 ): 1 - 6 .

杨红英 , 冯双磊 , 王勃 , 等 . 基于线性回归的风电功率预测误差修正方法 [J]. 电力系统及其自动化学报 , 2013 , 25 ( 4 ): 14 - 17 .

YANG Hongying , FENG Shuanglei , WANG Bo , et al . Study of the MOS method based on linear regression for wind power prediction [J]. Proceedings of the CSU-EPSA , 2013 , 25 ( 4 ): 14 - 17 .

王彩霞 , 鲁宗相 , 乔颖 , 等 . 基于非参数回归模型的短期风电功率预测 [J]. 电力系统自动化 , 2010 , 34 ( 16 ): 78 - 82 .

WANG Caixia , LU Zongxiang , QIAO Ying , et al . Short-term wind power forecast based on non-parametric regression model [J]. Automation of Electric Power Systems , 2010 , 34 ( 16 ): 78 - 82 .

徐龙博 , 王伟 , 张滔 , 等 . 基于神经网络平均影响值的超短期风电功率预测 [J]. 电力系统自动化 , 2017 , 41 ( 21 ): 40 - 45 .

XU Longbo , WANG Wei , ZHANG Tao , et al . Ultra-short-term wind power prediction based on neural network and mean impact value [J]. Automation of Electric Power Systems , 2017 , 41 ( 21 ): 40 - 45 .

凌武能 , 杭乃善 , 李如琦 . 基于云支持向量机模型的短期风电功率预测 [J]. 电力自动化设备 , 2013 , 33 ( 7 ): 34 - 38 .

LING Wuneng , HANG Naishan , LI Ruqi . Short-term wind power forecasting based on cloud SVM model [J]. Electric Power Automation Equipment , 2013 , 33 ( 7 ): 34 - 38 .

王子赟 , 纪志成 . 基于灰色-辨识模型的风电功率短期预测 [J]. 电力系统保护与控制 , 2013 , 41 ( 12 ): 79 - 85 .

WANG Ziyun , JI Zhicheng . Grey-identification model based wind power generation short-term prediction [J]. Power System Protection and Control , 2013 , 41 ( 12 ): 79 - 85 .

王贺 , 胡志坚 , 陈珍 , 等 . 基于集合经验模态分解和小波神经网络的短期风功率组合预测 [J]. 电工技术学报 , 2013 , 28 ( 9 ): 137 - 144 .

WANG He , HU Zhijian , CHEN Zhen , et al . A hybrid model for wind power forecasting based on ensemble empirical mode decomposition and wavelet neural networks [J]. Transactions of China Electrotechnical Society , 2013 , 28 ( 9 ): 137 - 144 .

叶林 , 朱倩雯 , 赵永宁 . 超短期风电功率预测的自适应指数动态优选组合模型 [J]. 电力系统自动化 , 2015 , 39 ( 20 ): 12 - 18 .

YE Lin , ZHU Qianwen , ZHAO Yongning . Dynamic optimal combination model considering adaptive exponential for ultra-short term wind power prediction [J]. Automation of Electric Power Systems , 2015 , 39 ( 20 ): 12 - 18 .

WU Wenbin , PENG Mugen . 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 .

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 .

SUN Gaiping , JIANG Chuanwen , CHENG Pan , et al . Short-term wind power forecasts by a synthetical similar time series data mining method [J]. Renewable Energy , 2018 , 115 : 575 - 584 .

王勃 , 冯双磊 , 刘纯 . 基于天气分型的风电功率预测方法 [J]. 电网技术 , 2014 , 38 ( 1 ): 93 - 98 .

WANG Bo , FENG Shuanglei , LIU Chun . Study on weather typing based wind power prediction [J]. Power System Technology , 2014 , 38 ( 1 ): 93 - 98 .

阎洁 , 许成志 , 刘永前 . 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 . DOI: 10.7500/AEPS20170605001 http://dx.doi.org/10.7500/AEPS20170605001 .

YAN Jie , XU Chengzhi , LIU Yongqian . Short-term wind power prediction method base on wind speed cloud model in similar day [J]. Automation of Electric Power Systems , 2018 , 42 ( 6 ): 53 - 59 . DOI: 10.7500/AEPS20170605001 http://dx.doi.org/10.7500/AEPS20170605001 .

LI Yaqiong , DAI Chaobo , WANG Tongxun . Separate wind power and ramp predictions based on meteorological variables and clustering method [C]// IEEE 6th International Conference on Power Systems (ICPS), March 4-6 , 2016 , New Delhi , India: 6p .

熊音笛 , 刘开培 , 秦亮 . 基于时序数据动态天气划分的短期风电功率预测方法 [J]. 电网技术， 2019 ， 43 （ 9 ）： 3353 - 3359 .

XIONG Yindi , LIU Kaipei , QIN Liang . Short-term wind power prediction method based on dynamic wind power weather division of time sequence data [J]. Power System Technology ， 2019 ， 43 （ 9 ）： 3353 - 3359 .

AORAN X V , YANG Tao , JI Jianwei , et al . Application of fuzzy clustering algorithm in the evaluation of abandoned wind power [C]// Chinese Control and Decision Conference (CCDC) , June 9 - 11 , 2018 , Shenyang, China: 5256 - 5259 .

XU Qianyao , HE Dawei , ZHANG Ning , et al . A short-term wind power forecasting approach with adjustment of numerical weather prediction input by data mining [J]. IEEE Transactions on Sustainable Energy , 2015 , 6 ( 4 ): 1283 - 1291 .

郁琛 , 薛禹胜 , 文福拴 , 等 . 按时序特征优化模型后在线选配的超短期风电预测 [J]. 电力系统自动化 , 2015 , 39 ( 8 ): 5 - 11 .

YU Chen , XUE Yusheng , WEN Fushuan , et al . An ultra-short-term wind power prediction method using “offline classification and optimization, online model matching” based on time series features [J]. Automation of Electric Power Systems , 2015 , 39 ( 8 ): 5 - 11 .

叶林 , 路朋 , 滕景竹 , 等 . 考虑风电功率爬坡的功率预测-校正模型 [J]. 电力系统自动化 , 2019 , 43 ( 6 ): 49 - 56 . DOI: 10.7500/AEPS20180321004 http://dx.doi.org/10.7500/AEPS20180321004 .

YE Lin , LU Peng , TENG Jingzhu , et al . Power prediction and correction model considering wind power ramping events [J]. Automation of Electric Power Systems , 2019 , 43 ( 6 ): 49 - 56 . DOI: 10.7500/AEPS20180321004 http://dx.doi.org/10.7500/AEPS20180321004 .

陈宁 , 薛禹胜 , 丁杰 , 等 . 利用空间相关性的超短期风速预测 [J]. 电力系统自动化 , 2017 , 41 ( 12 ): 124 - 130 .

CHEN Ning , XUE Yusheng , DING Jie , et al . Ultra-short term wind speed prediction using spatial correlation [J]. Automation of Electric Power Systems , 2017 , 41 ( 12 ): 124 - 130 .

张良均 . Python数据分析与挖掘实战 [M]. 北京 : 机械工业出版社 , 2016 .

ZHANG Liangjun . Python data analysis and mining practice [M]. Beijing : China Machine Press , 2016 .

刘思峰 , 蔡华 , 杨英杰 , 等 . 灰色关联分析模型研究进展 [J]. 系统工程理论与实践 , 2013 , 33 ( 8 ): 2041 - 2046 .

LIU Sifeng , CAI Hua , YANG Yingjie , et al . Advance in grey incidence analysis modelling [J]. Systems Engineering-Theory & Practice , 2013 , 33 ( 8 ): 2041 - 2046 .

范高锋 , 王伟胜 , 刘纯 , 等 . 基于人工神经网络的风电功率预测 [J]. 中国电机工程学报 , 2008 , 28 ( 34 ): 118 - 123 .

FAN Gaofeng , WANG Weisheng , LIU Chun , et al . Wind power prediction based on artificial neural network [J]. Proceedings of the CSEE , 2008 , 28 ( 34 ): 118 - 123 .

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