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

ABSTRACT

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.

KEYWORDS:

• Short-term wind speed
• chaotic characteristic
• time scale
• time series
• 0-1 algorithm for chaos

Nomenclature

Table

Additional information

Funding

This work is partially supported by the Science Research Project of Liaoning Education Department [Grant No. LGD2016009] and Natural Science Foundation of Liaoning Province [Grant No. 20170540686].

Notes on contributors

Zhongda Tian

Zhongda Tian He received the B. Sc. degree in communication engineering from Liaoning University, China in 2001, received the M. Sc. degree in communication and information system from Northeastern University, China in 2004, and received the Ph.D degree in control theory and control engineering from Northeastern University, China in 2013. He is currently an associate professor in College of Information Science and Engineering, Shenyang University of Technology, China. His research interests include time series prediction, predictive control, delay compensation and scheduling for networked control system.