结合多重聚类和分层聚类的超短期风电功率预测方法

彭晨宇; 陈宁; 高丙团

doi:10.7500/AEPS20190105003

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结合多重聚类和分层聚类的超短期风电功率预测方法

研制与开发 | 更新时间：2025-06-16

- 结合多重聚类和分层聚类的超短期风电功率预测方法
- Ultra-short-term Wind Power Forecasting Method Combining Multiple Clustering and Hierarchical Clustering
- 电力系统自动化-方正平台 2020年44卷第2期页码：172-179
- 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：
  
  彭晨宇(1994—),男,硕士研究生,主要研究方向:新能源发电预测及控制。E-mail:i3908971@163.com
  陈宁(1983—),男,通信作者,博士,研究员级高级工程师,主要研究方向:新能源发电并网分析与运行控制。E-mail:chenning8375@163.com
  高丙团(1981—),男,博士,教授,博士生导师,主要研究方向:新能源发电运行控制。E-mail:gaobingtuan@seu.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(51741709)
- DOI：10.7500/AEPS20190105003
  中图分类号：
- 收稿日期：2019-01-05，
  
  修回日期：2019-04-14，
  
  网络出版日期：2019-07-30，
  
  纸质出版日期：2020-01-25
- 稿件说明：
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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

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