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Analysis of multi-scale chaotic characteristics of wind power based on Hilbert–Huang transform and Hurst analysis

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  • Liang, Zhengtang
  • Liang, Jun
  • Zhang, Li
  • Wang, Chengfu
  • Yun, Zhihao
  • Zhang, Xu
Abstract
The causes of uncertainty in wind farm power generation are not yet fully understood. A method for the scale division of wind power based on the Hilbert–Huang transform (HHT) and Hurst analysis is proposed in this paper, which allows the various multi-scale chaotic characteristics of wind power to be investigated to reveal further information about the dynamic behavior of wind power. First, the time–frequency characteristics of wind power are analyzed using the HHT, and then Hurst analysis is applied to analyze the stochastic/persistent characteristics of the different time–frequency components. Second, based on their fractal structures, the components are superposed and reconstructed into three series, which are defined as the Micro-, Meso- and Macro-scale subsequences. Finally, indices related to the statistical and behavioral characteristics of the subsequences are calculated and used to analyze their nonlinear dynamic behavior. The data collected from a wind farm of Hebei Province, China, are selected for case studies. The simulation results reveal that (1) although the time–frequency components can be decomposed, the different fractal structures of the signal are also derived from the original series; (2) the three scale subsequences all present chaotic characteristics and each of them exhibits its own unique properties. The Micro-scale subsequence shows strong randomness and contributes the least to the overall fluctuations; the Macro-scale subsequence is the steadiest and exhibits the most significant tendency; the Meso-scale subsequence which possesses the greatest variance contribution rate and the maximum largest Lyapunov exponent, is the dominant factor driving the fluctuation and dynamic behavior of wind power; (3) the short-term predictions of these three subsequences based on extreme learning machine (ELM) and least-squares support vector machine (LSSVM) models have validated the above analysis results, which show that the number of steps of look-ahead predictability have pursued an ordinal trend in term of the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) and the prediction error contribution rate of the Meso-scale subsequence is the maximum. Furthermore, the short-term wind power forecasting of 6-step-ahead based on the multi-scale analysis is performed by EMD-LSSVM+ELM and the normalized Mean Absolute Error (nMAE) and normalized Root Mean Square Error (nRMSE) have been decreased by 49.45% and 44.30% compared with those of LSSVM, and 37.96% and 27.12% compared with those of EMD-LSSVM, respectively.

Suggested Citation

  • Liang, Zhengtang & Liang, Jun & Zhang, Li & Wang, Chengfu & Yun, Zhihao & Zhang, Xu, 2015. "Analysis of multi-scale chaotic characteristics of wind power based on Hilbert–Huang transform and Hurst analysis," Applied Energy, Elsevier, vol. 159(C), pages 51-61.
    • Handle: RePEc:eee:appene:v:159:y:2015:i:c:p:51-61
    DOI: 10.1016/j.apenergy.2015.08.111
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    1. Yu, Dayang & Liang, Jun & Han, Xueshan & Zhao, Jianguo, 2011. "Profiling the regional wind power fluctuation in China," Energy Policy, Elsevier, vol. 39(1), pages 299-306, January.
    2. Coughlin, Katie & Murthi, Aditya & Eto, Joseph, 2014. "Multi-scale analysis of wind power and load time series data," Renewable Energy, Elsevier, vol. 68(C), pages 494-504.
    3. Kavasseri, Rajesh G. & Nagarajan, Radhakrishnan, 2005. "A multifractal description of wind speed records," Chaos, Solitons & Fractals, Elsevier, vol. 24(1), pages 165-173.
    4. Deane, J.P. & Drayton, G. & Ó Gallachóir, B.P., 2014. "The impact of sub-hourly modelling in power systems with significant levels of renewable generation," Applied Energy, Elsevier, vol. 113(C), pages 152-158.
    5. Georgilakis, Pavlos S., 2008. "Technical challenges associated with the integration of wind power into power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(3), pages 852-863, April.
    6. Chang, Tian Pau, 2011. "Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application," Applied Energy, Elsevier, vol. 88(1), pages 272-282, January.
    7. Rehman, S. & Siddiqi, A.H., 2009. "Wavelet based hurst exponent and fractal dimensional analysis of Saudi climatic dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 40(3), pages 1081-1090.
    8. Liu, Hui & Tian, Hong-qi & Pan, Di-fu & Li, Yan-fei, 2013. "Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks," Applied Energy, Elsevier, vol. 107(C), pages 191-208.
    9. Tsekouras, Georgios & Koutsoyiannis, Demetris, 2014. "Stochastic analysis and simulation of hydrometeorological processes associated with wind and solar energy," Renewable Energy, Elsevier, vol. 63(C), pages 624-633.
    10. Wang, J. & Botterud, A. & Bessa, R. & Keko, H. & Carvalho, L. & Issicaba, D. & Sumaili, J. & Miranda, V., 2011. "Wind power forecasting uncertainty and unit commitment," Applied Energy, Elsevier, vol. 88(11), pages 4014-4023.
    11. Carapellucci, Roberto & Giordano, Lorena, 2013. "The effect of diurnal profile and seasonal wind regime on sizing grid-connected and off-grid wind power plants," Applied Energy, Elsevier, vol. 107(C), pages 364-376.
    12. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.
    13. Chang, Tian-Pau & Ko, Hong-Hsi & Liu, Feng-Jiao & Chen, Pai-Hsun & Chang, Ying-Pin & Liang, Ying-Hsin & Jang, Horng-Yuan & Lin, Tsung-Chi & Chen, Yi-Hwa, 2012. "Fractal dimension of wind speed time series," Applied Energy, Elsevier, vol. 93(C), pages 742-749.
    14. Chen, Chien-chih & Lee, Ya-Ting & Chang, Young-Fo, 2008. "A relationship between Hurst exponents of slip and waiting time data of earthquakes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(18), pages 4643-4648.
    15. Akdag, S.A. & Bagiorgas, H.S. & Mihalakakou, G., 2010. "Use of two-component Weibull mixtures in the analysis of wind speed in the Eastern Mediterranean," Applied Energy, Elsevier, vol. 87(8), pages 2566-2573, August.
    16. Jung, Jaesung & Tam, Kwa-Sur, 2013. "A frequency domain approach to characterize and analyze wind speed patterns," Applied Energy, Elsevier, vol. 103(C), pages 435-443.
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