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Mixture distribution and multifractal analysis applied to wind speed in the Brazilian Northeast region

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  • Santos, Fábio Sandro dos
  • Nascimento, Kerolly Kedma Felix do
  • Jale, Jader da Silva
  • Stosic, Tatijana
  • Marinho, Manoel H.N.
  • Ferreira, Tiago A.E.

Abstract

The growing investments and installations of wind farms in the Brazilian Northeast have drawn attention to the region, leading investors and researchers to seek better ways of using the local wind regimen for energy production. In face of the complex behavior of wind speed time series, mixture distribution models have been applied to bimodal databases aiming at achieving the best modeling for series fitting. This paper used data from stations located in the nine states that make up the Brazilian Northeast region (Maranhão, Piauí, Ceará, Rio Grande do Norte, Paraíba, Pernambuco, Alagoas, Sergipe, and Bahia) between January 1st, 2004 and August 29th, 2018. The two-component Weibull distribution model was employed to model the historical series using the Expectation Maximization (EM) algorithm to search for optimal parameters in data distribution. Multifractal detrended fluctuation analysis was applied to verify series persistence over time and, using spatialization obtained with inverse distance weighting, the results were estimated at the sites lacking meteorological wind information. The results obtained indicate that the highest mean wind speeds are found in the states of Rio Grande do Norte, Paraíba, and Pernambuco, whereas the lowest occur in parts of Bahia, Piauí, and Maranhão. The highest mean wind speeds were recorded between 10 a.m. and 8 p.m. of each day at every station. Multifractal analysis revealed that wind speed series exhibit persistent overall behavior for all stations, with multifractality dominated by small fluctuations. For most of the stations both long term correlations and broad probability density function of wind speed values are found to cause multifractality of the process. This study allows identifying favorable areas for the installation of wind farms in different locations of the Brazilian Northeast region.

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  • Santos, Fábio Sandro dos & Nascimento, Kerolly Kedma Felix do & Jale, Jader da Silva & Stosic, Tatijana & Marinho, Manoel H.N. & Ferreira, Tiago A.E., 2021. "Mixture distribution and multifractal analysis applied to wind speed in the Brazilian Northeast region," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    • Handle: RePEc:eee:chsofr:v:144:y:2021:i:c:s0960077921000047
    DOI: 10.1016/j.chaos.2021.110651
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    as
    1. Kruyt, Bert & Lehning, Michael & Kahl, Annelen, 2017. "Potential contributions of wind power to a stable and highly renewable Swiss power supply," Applied Energy, Elsevier, vol. 192(C), pages 1-11.
    2. Gruber, Katharina & Klöckl, Claude & Regner, Peter & Baumgartner, Johann & Schmidt, Johannes, 2019. "Assessing the Global Wind Atlas and local measurements for bias correction of wind power generation simulated from MERRA-2 in Brazil," Energy, Elsevier, vol. 189(C).
    3. Grech, Dariusz, 2016. "Alternative measure of multifractal content and its application in finance," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 183-195.
    4. Bracale, Antonio & Carpinelli, Guido & De Falco, Pasquale, 2017. "A new finite mixture distribution and its expectation-maximization procedure for extreme wind speed characterization," Renewable Energy, Elsevier, vol. 113(C), pages 1366-1377.
    5. Delbianco, Fernando & Tohmé, Fernando & Stosic, Tatijana & Stosic, Borko, 2016. "Multifractal behavior of commodity markets: Fuel versus non-fuel products," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 573-580.
    6. Shin, Ju-Young & Ouarda, Taha B.M.J. & Lee, Taesam, 2016. "Heterogeneous mixture distributions for modeling wind speed, application to the UAE," Renewable Energy, Elsevier, vol. 91(C), pages 40-52.
    7. Balkissoon, Sarah & Fox, Neil & Lupo, Anthony, 2020. "Fractal characteristics of tall tower wind speeds in Missouri," Renewable Energy, Elsevier, vol. 154(C), pages 1346-1356.
    8. Safari, Bonfils, 2011. "Modeling wind speed and wind power distributions in Rwanda," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(2), pages 925-935, February.
    9. Kumar, Satish & Cuntz, Manfred & Musielak, Zdzislaw E., 2015. "Fractal and multifractal analysis of the rise of oxygen in Earth’s early atmosphere," Chaos, Solitons & Fractals, Elsevier, vol. 77(C), pages 296-303.
    10. Arellano-Valle, Reinaldo B. & Ferreira, Clécio S. & Genton, Marc G., 2018. "Scale and shape mixtures of multivariate skew-normal distributions," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 98-110.
    11. Telesca, Luciano & Lovallo, Michele & Kanevski, Mikhail, 2016. "Power spectrum and multifractal detrended fluctuation analysis of high-frequency wind measurements in mountainous regions," Applied Energy, Elsevier, vol. 162(C), pages 1052-1061.
    12. Carta, J.A. & Ramírez, P., 2007. "Analysis of two-component mixture Weibull statistics for estimation of wind speed distributions," Renewable Energy, Elsevier, vol. 32(3), pages 518-531.
    13. Wang, Qizhen, 2019. "Multifractal characterization of air polluted time series in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 167-180.
    14. Kalamaras, N. & Philippopoulos, K. & Deligiorgi, D. & Tzanis, C.G. & Karvounis, G., 2017. "Multifractal scaling properties of daily air temperature time series," Chaos, Solitons & Fractals, Elsevier, vol. 98(C), pages 38-43.
    15. Laib, Mohamed & Golay, Jean & Telesca, Luciano & Kanevski, Mikhail, 2018. "Multifractal analysis of the time series of daily means of wind speed in complex regions," Chaos, Solitons & Fractals, Elsevier, vol. 109(C), pages 118-127.
    16. Zhang, Xin & Yang, Liansheng & Zhu, Yingming, 2019. "Analysis of multifractal characterization of Bitcoin market based on multifractal detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 973-983.
    17. Zhang, Xie & Liu, Hongzhi & Zhao, Yifei & Zhang, Xingchen, 2019. "Multifractal detrended fluctuation analysis on air traffic flow time series: A single airport case," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    18. Wang, Jianzhou & Huang, Xiaojia & Li, Qiwei & Ma, Xuejiao, 2018. "Comparison of seven methods for determining the optimal statistical distribution parameters: A case study of wind energy assessment in the large-scale wind farms of China," Energy, Elsevier, vol. 164(C), pages 432-448.
    19. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
    20. Hu, Qinghua & Wang, Yun & Xie, Zongxia & Zhu, Pengfei & Yu, Daren, 2016. "On estimating uncertainty of wind energy with mixture of distributions," Energy, Elsevier, vol. 112(C), pages 935-962.
    21. Laib, Mohamed & Telesca, Luciano & Kanevski, Mikhail, 2018. "Periodic fluctuations in correlation-based connectivity density time series: Application to wind speed-monitoring network in Switzerland," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 1555-1569.
    22. Stosic, Dusan & Stosic, Darko & de Mattos Neto, Paulo S.G. & Stosic, Tatijana, 2019. "Multifractal characterization of Brazilian market sectors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 956-964.
    23. Gajardo, Gabriel & Kristjanpoller, Werner, 2017. "Asymmetric multifractal cross-correlations and time varying features between Latin-American stock market indices and crude oil market," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 121-128.
    24. Santos, J.V.C. & Moreira, D.M. & Moret, M.A. & Nascimento, E.G.S., 2019. "Analysis of long-range correlations of wind speed in different regions of Bahia and the Abrolhos Archipelago, Brazil," Energy, Elsevier, vol. 167(C), pages 680-687.
    25. Baranowski, Piotr & Gos, Magdalena & Krzyszczak, Jaromir & Siwek, Krzysztof & Kieliszek, Adam & Tkaczyk, Przemysław, 2019. "Multifractality of meteorological time series for Poland on the base of MERRA-2 data," Chaos, Solitons & Fractals, Elsevier, vol. 127(C), pages 318-333.
    26. de Benicio, Rosilda B. & Stošić, Tatijana & de Figueirêdo, P.H. & Stošić, Borko D., 2013. "Multifractal behavior of wild-land and forest fire time series in Brazil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6367-6374.
    27. Allouhi, A. & Zamzoum, O. & Islam, M.R. & Saidur, R. & Kousksou, T. & Jamil, A. & Derouich, A., 2017. "Evaluation of wind energy potential in Morocco's coastal regions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 311-324.
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    2. dos Santos, Fábio Sandro & do Nascimento, Kerolly Kedma Felix & da Silva Jale, Jader & Xavier, Sílvio Fernando Alves & Ferreira, Tiago A.E., 2024. "Brazilian wind energy generation potential using mixtures of Weibull distributions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).

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