风电场风速时间序列的多重分形去趋势波动分析

Multi-fractal Detrended Fluctuation Analysis of Wind Speed Time Series in Wind Farm

为探索风电场风速时间序列的标度不变性,采用多重分形去趋势波动分析方法(MF-DFA)对风速时间序列进行分析。通过计算广义Hurst指数、尺度函数、多重分形谱,细致量化了风速序列的局部和不同层次的波动奇异性,并考察了多重分形参数对风速预测的影响。研究结果表明:风速时间序列的波动具有长程相关性,且呈现显著多重分形特征;多重分形参数与风速变化存在一定的关联性,采用多重分形谱可在一定程度上对风速的变化趋势进行预测,且风速波动量越大,预测的结果越准确。

In order to detect the scale qualities of wind speed time series, the wind speed time series are analyzed by multi-fractal detrended fluctuation analysis(MF-DFA) method. Detailed quantitative the volatility singularity of partial and different levels of the wind speed time series are calculated by the generalized Hurst index, scaling function and multi-fractal spectrum, and the effects of multi-fractal parameters on wind speed prediction are considered. The analysis results show that fluctuation of wind speed time series have log-range correlation and significant multi-fractal characteristics. The multi-fractal parameters associated with the change of wind speed, to some degree, can be predicted by multi-fractal spectrums, and the prediction becomes more accurate with the more fluctuation of the wind speed.