Evidence of Crossover Phenomena in Wind-Speed Data

Abstract

In this report, a systematic analysis of hourly wind speed data obtained from three potential wind generation sites (in North Dakota) is analyzed. The power spectra of the data exhibited a power-law decay characteristic of 1/f α processes with possible long-range correlations. Conventional analysis using Hurst exponent estimators proved to be inconclusive. Subsequent analysis using detrended fluctuation analysis (DFA) revealed a crossover in the scaling exponent (α). At short time scales, a scaling exponent of α ∼ 1.4 indicated that the data resembled Brownian noise, whereas for larger time scales the data exhibited long range correlations (α ∼ 0.7). The scaling exponents obtained were similar across the three locations. Our findings suggest the possibility of multiple scaling exponents characteristic of multifractal signals.

Wind power forecasting : state-of-the-art 2009.

“…The forecasted wind speed is converted to wind power by using a manufacturer's power curve. Following the results presented in [101], the authors suggest a modified ARIMA model (f-ARIMA) to deal with long-range correlations (LRCs). An LRC process is characterized by a slow decay of the autocorrelation function.…”

Applicatıon of detrended fluctuation analysis to temperature data from Turkey

“…Some of these studies (Koscielny- Bunde et al, 1998;Bunde et al, 2001;Govindan et al, 2003) claim that the scaling exponent is universal for the temperature data while the others Kurnaz, 2004;Pattanyús-Ábrahám et al, 2004;Kiràly and Jánosi, 2005;Bartos and Jánosi, 2006;Kiràly et al, 2006;Rybski et al, 2008) claim the opposite. Apart from temperature data, the DFA analysis has also been applied to some meteorological and climatological variables such as wind speed (Govindan and Kantz, 2004;Kavasseri and Nagarajan, 2004), relative humidity (Chen et al, 2007), cloud breaking (Ivanova and Ausloos, 1999) and NAO (North Atlantic Oscillation) index (Caldeira et al, 2007). In addition to these studies based on the DFA, there are many other studies based on non-DFA techniques searching the long-term correlation of temperature data (See, e.g.…”

Quantifying signals with power-law correlations: A comparative study of detrended fluctuation analysis and detrended moving average techniques

“…There is growing evidence that output signals of many physical [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15], biological [16,17,18,19], physiological [20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35] and economic systems [36,37,38,39,40,41,42,43], where multiple component feedback interactions play a central role, exhibit complex self-similar fluctuations over a broad range of space and/or time scales. These fluctuating signals can be characterized by long-range power-law correlations.…”