风电场风速时间序列的复杂动力学特性分析

Complex Dynamical Analysis of Wind Speed Time Series in Wind Farm

摘要:

利用混沌理论对风电场风速数据进行了相空间重构,首先由CC方法计算出嵌入维数和延迟时间,然后采用GP算法计算出吸引子关联维数,最后用小数据量改进算法得出风速时间序列的最大Lyapunov指数,由计算结果发现风电场风速时间序列具有混沌特性,为利用混沌预测方法进一步提高风速预测精度提供参考.

Abstract:

Wind speed data was reconstructed in phasespace based on chaos theory.The embedding dimension and delay time were first calculated via the CC method.The correlative dimension of attractor was then calculated with the GP method.Finally,the largest Lyapunov exponent of wind speed time series was calculated on the basis of the improved small data sets method.The wind speed is found to be of chaotic property,therefore,the chaosbased prediction method is recommanded to further improve the prediction accuracy of wind speed time series in wind farm.