基于改进EEMD-SE-ARMA的超短期风功率组合预测模型  

Wind power ultra short-term model based on improved EEMD-SE-ARMA

作　　者：田波[1] 朴在林[1] 郭丹[1] 王慧[1] 

机构地区：[1]沈阳农业大学信息与电气工程学院,辽宁沈阳110866

出　　处：《电力系统保护与控制》2017年第4期72-79,共8页Power System Protection and Control

基　　金：十二五国家科技支撑项目(2012BAJ26B00)~~

摘　　要：针对风力发电功率时间序列具有非线性和非平稳性的特性,提出了一种改进的集成经验模态分解(Modified Ensemble Empirical Mode Decomposition,MEEMD)-样本熵(Sample Entropy,SE)-ARMA的风电功率超短期组合预测模型。将EEMD分解中添加的白噪声信号改为添加绝对值相等的正负两组白噪声信号,并将MEEMD分解过程中的EMD步骤使用端点延拓和分段三次埃尔米特插值进行改进,形成一种改进的EEMD分解算法(即MEEMD)。利用MEEMD-SE将风力发电功率时间序列分解为一系列复杂度差异明显的风电子序列;针对每一个不同的子序列建立适当的ARMA预测模型;将各预测分量进行叠加重构,得到最终的风电功率预测值。通过算例分析及与其他几种预测模型预测结果的对比,证明MEEMD-SE-ARMA组合预测模型可以有效地提高风力发电功率超短期预测的精度。In view of the nonlinear and non-stationary characteristics of wind power time series, this paper presents a modified ensemble empirical mode decomposition（MEEMD）-sample entropy（SE）-ARMA wind power ultra short term combined forecasting model. The white noise signal added in the EEMD decomposition is changed to two groups positive and negative white noise signal which have the equal absolute value, and the EMD steps of the decomposition process of MEEMD is improved with the endpoint extension and three piecewise Hermite interpolation, forming a modified EEMD decomposition algorithm（MEEMD）. The wind power time series is decomposed into a series of complex wind power generation by MEEMD-SE, and the ARMA forecasting model is built for each different sub sequence, and the final wind power forecast value is obtained. Through numerical analysis and comparison with other forecasting models, the results show that the MEEMD-SE-ARMA combination forecasting model can effectively improve the accuracy of the ultra short term forecasting of wind power generation.

关 键 词：改进的集成经验模态分解 风电预测 样本熵 时间序列 组合预测模型 端点延拓 

分 类 号：TM614[电气工程—电力系统及自动化]