摘要
准确、全面的了解风电场有功出力特性是高效利用风能资源的前提,然而风电场出力数据由于其受自然来风影响而存在剧烈的波动性与随机性,针对于此,文章首先在对风电场出力特性进行描述的基础上,引入了自适应的数据分解方法—集合经验模态分解(EEMD),利用EEMD将采样得到的风电场非线性、非稳定有功出力的时间序列数据分解为对应的若干个本征模函数IMF,并通过观察分解所得的本征模函数IMF以及其各分量的波动情况来深入了解采样区域的风电出力特性,以期通过此方法为未来更好、更高效的利用大规模风电提供新的思路。
Accurate and comprehensive understanding of the wind power output characteristics is the precondition of efficient utilization of wind energy resources,wind power output,however,due to the affected by the natural wind to exist severe volatility and randomness. For this,the wind power output characteristic is described first in this article,and then an adaptive data decomposition method is introduced,that is Ensemble Empirical Mode Decomposition（ EEMD）,the nonlinear and unstable active output of time series data is decomposed into several intrinsic mode functions（ IMF） by EEMD,by observing the decomposition of the IMF and the volatility of each component to deeply understand the wind power output characteristics of sampling area,hoping it will be a new thought that better and more efficient use of large scale wind power in the future.
出处
《电测与仪表》
北大核心
2016年第23期16-22,共7页
Electrical Measurement & Instrumentation
