摘要
针对风电场中风速随机性大,难以准确和高效预测的问题,提出一种基于密度峰值聚类的风电场风速预测方法。该方法首先对风电机组采用密度峰值算法进行聚类,随后采用长短期记忆网络模型,对同类风电机组的风速进行预测。考虑到实际聚类时各指标存在不等重要性的情况,基于加权理论对数据进行了预处理,同时通过用主成分分析对数据进行降维,避免了密度峰值聚类面对高维数据时聚类效果差的现象。最后根据风电场实测数据对该方法的有效性进行了验证,实验结果表明,该方法具有较高的预测精度。
In order to solve the problem that wind speed in wind farms is difficult to be predicted accurately and efficiently due to its randomness,this study proposes a wind speed prediction method for wind farms based on density peaks clustering. Utilizing this method,wind turbines are firstly clustered based on density peak algorithm. Subsequently,the long short-term memory network models are used to predict the wind speed of similar wind turbines. Considering the unequal importance of each indicator during actual clustering,the data are preprocessed based on weighting theory. At the same time,the dimension reduction of data is realized through the principal component analysis in order that a poor effect of density peak clustering can be avoided when facing high-dimensional data.Finally,the effectiveness of the proposed method is verified based on the measured data of the wind farm. Experimental results show that the method has relatively high predicting accuracy.
作者
王东风
李嘉宇
黄宇
侯伟珍
张妍
Wang Dongfeng;Li Jiayu;Huang Yu;Hou Weizhen;Zhang Yan(Department of Automation,North China Electric Power University,Baoding 071003,China)
出处
《太阳能学报》
EI
CAS
CSCD
北大核心
2021年第12期110-118,共9页
Acta Energiae Solaris Sinica
基金
中央高校基本科研业务费专项资金(2019MS089)。
关键词
风电场
风电机组
聚类算法
风速预测
长短期记忆网络
主成分分析
wind farms
wind turbines
clustering algorithms
wind speed prediction
long short-term memory
principal component analysis
作者简介
通信作者:张妍(1980-),女,博士、讲师,主要从事风电场风电功率预测方面的研究。zhangyan_07@126.com。
