Asymmetric multiscale multifractal analysis of wind speed signals

Xiaonei Zhang (), Ming Zeng and Qinghao Meng ()
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Xiaonei Zhang: School of Electrical Automation and Information Engineering, Tianjin University, Tianjin 300072, P. R. China
Ming Zeng: School of Electrical Automation and Information Engineering, Tianjin University, Tianjin 300072, P. R. China
Qinghao Meng: School of Electrical Automation and Information Engineering, Tianjin University, Tianjin 300072, P. R. China

International Journal of Modern Physics C (IJMPC), 2017, vol. 28, issue 11, 1-26

Abstract: We develop a new method called asymmetric multiscale multifractal analysis (A-MMA) to explore the multifractality and asymmetric autocorrelations of the signals with a variable scale range. Three numerical experiments are provided to demonstrate the effectiveness of our approach. Then, the proposed method is applied to investigate multifractality and asymmetric autocorrelations of difference sequences between wind speed fluctuations with uptrends or downtrends. The results show that these sequences appear to be far more complex and contain abundant fractal dynamics information. Through analyzing the Hurst surfaces of nine difference sequences, we found that all series exhibit multifractal properties and multiscale structures. Meanwhile, the asymmetric autocorrelations are observed in all variable scale ranges and the asymmetry results are of good consistency within a certain spatial range. The sources of multifractality and asymmetry in nine difference series are further discussed using the corresponding shuffled series and surrogate series. We conclude that the multifractality of these series is due to both long-range autocorrelation and broad probability density function, but the major source of multifractality is long-range autocorrelation, and the source of asymmetry is affected by the spatial distance.

Keywords: Asymmetric multiscale multifractal analysis (A-MMA); asymmetric autocorrelations; difference sequences of wind speed fluctuations; spatial distance (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0129183117501376

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