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文章摘要
基于MEEMD-KELM的短期风电功率预测
Short-term prediction of wind power based on MEEMD-KELM
Received:May 28, 2019  Revised:May 28, 2019
DOI：10.19753/j.issn1001-1390.2020.21.013
中文关键词: MEEMD  KELM  风电功率预测  排列熵  模态混淆
英文关键词: MEEMD, KELM, prediction  of wind  power, permutation  entropy, mode  mixing
基金项目:国家自然科学基金(11302123)；上海市浦江人才计划（15PJ1402500）
Author NameAffiliationE-mail
Zhao Ruizhi School of Electrical Engineering,Shanghai Dianji University 845003063@qq.com 
Ding Yunfei* School of Electrical Engineering,Shanghai Dianji University dingyf@sdju.edu.cn 
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中文摘要:
      风电功率时序信号是间歇性、波动性的非平稳信号,信号的平稳化处理是风电功率预测的关键。针对EEMD在分解风功率时序信号时存在模态混淆、伪分量和较大的重构误差等问题,将MEEMD用于风功率信号分解并与KELM模型相结合,提出了基于MEEMD-KELM的风电功率短期预测方法。该方法首先采用CEEMD将原始信号按频率高低依次分解,再检测分量的排列熵值,通过熵值判断异常分量信号并将其从原始信号中剔除,再对分离后的信号进行EMD分解,得到的若干个IMF分量分别通过KELM模型进行组合预测。以上海某风场为例进行仿真实验,并与传统方法进行对比,结果表明该方法预测精度更优且更具稳定性。
英文摘要:
      
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