Chaotic characteristic analysis of short-term wind speed time series with different time scales

Characteristic analysis of short-term wind speed time series is very meaningful for wind power system. The dynamic behavior of short-term wind speed time series is an external manifestation under the combined action of complex non-linear and multi-scale phenomena. Based on the chaotic theory, the chaotic characteristics of short-term wind speed time series with different time scales are analyzed and discussed. Firstly, the 0-1 test algorithm for chaos is used to calculate the incremental growth rate of short-term wind speed time series. The variation of chaotic characteristics of short-term wind speed with different time scales is discussed. Then, the phase space of short-term wind speed time series with different time scales is reconstructed, and the correlation dimension, the largest Lyapunov exponent, and Kolmogorov entropy are calculated, respectively. These three chaotic identification indexes are used to analyze the chaotic characteristics of short-term wind speed time series and their variation with time scales. The results show that the relationship between the timescale and the non-linear characteristics of short-term wind speed time series is not obvious. The incremental growth rate of short-term wind speed time series has no obvious change with the increase of timescale. There is no clear relationship between the change of timescale and embedding dimension and delay time. The largest Lyapunov exponent and Kolmogorov entropy increase with the increase of timescale, which means that the chaotic characteristics of short-term wind speed time series become stronger and the predictability becomes worse with the timescale.