基于EEMD和ARCH的风电功率超短期预测  

Ultra-short-term wind power forecasting based on EEMD and ARCH

作　　者：李乐[1] 刘天琪[1] 陈振寰[2] 王福军[2] 关铁英[2] 何川[1] 吴星[1] 

机构地区：[1]四川大学电气信息学院,成都610065 [2]国网甘肃省电力公司,兰州730030

出　　处：《电测与仪表》2015年第18期16-21,共6页Electrical Measurement & Instrumentation

基　　金：国网甘肃省电力公司科技项目(5227201350PM)

摘　　要：针对风电功率具有非平稳性和波动集群现象,提出一种基于集合经验模态分解和自回归条件异方差组合模型预测方法。该方法通过EEMD分解法将风电出力分解为一系列平稳的时序分量,再由游程判定法,将时序分量重组为波动分量、短期趋势分量和长期趋势分量,以集中分量特征信息降低预测难度;针对各分量的波动特征,建立相应的ARCH预测模型。算例结果表明,该种组合预测方法简单,具有较高的预测精度,能更好的反应风电功率的波动特性。The wind power has the qualifications of random and volatility concentration .This paper presents a com-bined model prediction method based on ensemble empirical mode decomposition （ EEMD） and autoregressive condi-tional heteroscedasticity model （ ARCH） .By means of the EEMD , the wind power sequence is decomposed into a se-ries of stationary components .Then the components are reconstructed into fluctuant components , medium-term trend and long-term trend components for centralizing the characteristic information and reducing the difficulty of predicting . Finally, considering the fluctuation characteristics of different types of components , the different ARCH models are built.Simulation results show that the combined prediction can offer more accurate forecasting results and reflect the fluctuation characteristics of wind power .

关 键 词：超短期预测 EEMD 游程检验法 ARCH 

分 类 号：TM936.9[电气工程—电力电子与电力传动]