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Journal of Ambient Intelligence and Humanized Computing, 15, 897–921p. (2023) DOI:10.1007/s12652-021-03343-1
With the continuous growth of wind power access capacity, the impact of intermittent and volatile wind power generation on the grid is becoming more and more obvious, so the research of wind power prediction has been widely concerned. The dynamic behavior of wind power time series is the external performance of complex nonlinear and multi-scale phenomena. Before choosing an appropriate prediction model, it is of great significance to analyze the characteristics of wind power time series for wind power grid system. At the same time, different sampling time scales of wind power also have an important impact on its dynamic characteristics. In this study, the chaotic dynamics behaviour of wind power time series at different time scales is discussed. The research methods include stationarity and white noise judgment, power spectral density analysis, autocorrelation function analysis, probability distribution, the Hurst index, 0–1 test algorithm for chaos, correlation dimension, maximum Lyapunov exponent, Kolmogorov entropy, recurrence plot, and information entropy method are adopted to study the chaotic dynamic behavior of wind power time series at different time scales. The actual wind power data of a wind farm at different time scales are taken as the research object for the case study. The case study results draw the following conclusions: (a) The wind power time series is non-stationary and non-white noise. (b) The direct-current and low frequency components parts store the main energy. (c) In the long-term, wind power is unpredictable. (d) The output level of wind power depends on the time scales. (e) The wind power time series obeys the fractal Brownian motion. (f) Wind power time series has chaotic characteristic. (g) Wind power time series has fractal characteristics. (h) As the time scales changes, so does the maximum prediction horizon. (i) With the decrease of time scales, the information loss rate has also declined. (j) Wind power at large time scales is more chaotic. The research results of this study have certain theoretical value and practical significance for grasping the fluctuation law of wind power and improving the prediction accuracy of wind power.
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