Multiscale entropy analysis of wind speed dynamics in Petrolina, Northeast Brazil
DOI:
https://doi.org/10.33448/rsd-v10i1.11460Keywords:
Wind speed; Multiscale entropy; Wind energy.Abstract
Purpose: In this paper, we analyzed the intra-annual variability of complexity of wind dynamics in Petrolina, Brazil and its relation with the wind potential. Methodology: We applied the Multiscale Sample Entropy (MSE) method on wind speed temporal series for each month of 2010. The data are recorded every 10 min at 50m height. Results: The results showed higher entropy values at higher temporal scales indicating that wind speed fluctuations are les regular and less predictable when wind speed is observed at lower temporal frequency. For all months, average wind speed is above a cut in level 3.5 m, the speed at which turbines start operating and producing electricity, indicating that the location of Petrolina is promising for wind energy generation. We also found that the wind speed is positively correlated with entropy values for all months when recorded at 10min frequency and between August and December when recorded t 1 h frequency. Conclusion: In these periods wind speed temporal fluctuations are more irregular, which is considered as unfavorable condition for the operation of wind turbines, leading to lower efficiency in the capture of wind energy for electricity production.
References
Ahmed, M. U., & Mandic, D. P. (2011). Multivariate multiscale entropy: A tool for complexity analysis of multichannel data. Physical Review E, 84(6), 61918. APS.
de Araujo Lima, L., & Bezerra Filho, C. R. (2010). Wind energy assessment and wind farm simulation in Triunfo–Pernambuco, Brazil. Renewable Energy, 35(12), 2705–2713. Elsevier.
Ayodele, T. R., & Ogunjuyigbe, A. S. O. (2016). Wind energy potential of Vesleskarvet and the feasibility of meeting the South African׳ s SANAE IV energy demand. Renewable and Sustainable Energy Reviews, 56, 226–234. Elsevier.
Balasis, G., Daglis, I. A., Papadimitriou, C., Kalimeri, M., Anastasiadis, A., & Eftaxias, K. (2009). Investigating dynamical complexity in the magnetosphere using various entropy measures. Journal of Geophysical Research: Space Physics, 114(A9). Wiley Online Library.
Behera, S., Sahoo, S., & Pati, B. B. (2015). A review on optimization algorithms and application to wind energy integration to grid. Renewable and Sustainable Energy Reviews, 48, 214–227. Elsevier.
Cavalcante, G., Vieira, F., Campos, E., Brandini, N., & Medeiros, P. R. P. (2020). Temporal streamflow reduction and impact on the salt dynamics of the São Francisco River Estuary and adjacent coastal zone (NE/Brazil). Regional Studies in Marine Science, 38, 101363. Elsevier.
Chou, C.-M. (2014). Complexity analysis of rainfall and runoff time series based on sample entropy in different temporal scales. Stochastic Environmental Research and Risk Assessment, 28(6), 1401–1408. Springer.
Costa, M., Goldberger, A. L., & Peng, C.-K. (2002). Multiscale entropy analysis of complex physiologic time series. Physical review letters, 89(6), 68102. APS.
Courtiol, J., Perdikis, D., Petkoski, S., Müller, V., Huys, R., Sleimen-Malkoun, R., & Jirsa, V. K. (2016). The multiscale entropy: Guidelines for use and interpretation in brain signal analysis. Journal of neuroscience methods, 273, 175–190. Elsevier.
Dutra, R. M., & Szklo, A. S. (2008). Incentive policies for promoting wind power production in Brazil: Scenarios for the Alternative Energy Sources Incentive Program (PROINFA) under the New Brazilian electric power sector regulation. Renewable Energy, 33(1), 65–76. Elsevier.
Faria, B. L. de, Justino, F. B., & Monteiro, L. I. B. (2011). Estudo do Potencial Eólico do Nordeste Brasileiro: uma alternativa para complementar a Matriz Energética durante o período de seca. XVII Congresso Brasileiro de Agrometeorologia. https://silo.tips/download/estudo-do-potencial-eolico-do-nordeste-brasileiro-uma-alternativa-para-complemen.
Gamboa, J. C. R., Marques, E. C. M., & Stosic, T. (2019). Complexity analysis of Brazilian agriculture and energy market. Physica A: Statistical Mechanics and its Applications, 523, 933–941. Elsevier.
Guzman-Vargas, L., Ramírez-Rojas, A., & Angulo-Brown, F. (2008). Multiscale entropy analysis of electroseismic time series. Natural Hazards and Earth System Sciences, 8(4), 855–860. Copernicus GmbH.
GWEC, G. W. E. C. (2019). Global Wind Report: Annual Market Update 2019. Retrieved March 6, 2020, from https://gwec.net/global-wind-report-2019
Koçak, K. (2009). Examination of persistence properties of wind speed records using detrended fluctuation analysis. Energy, 34(11), 1980–1985. Elsevier.
Kumar, M., Pachori, R. B., & Acharya, U. R. (2017). Automated diagnosis of myocardial infarction ECG signals using sample entropy in flexible analytic wavelet transform framework. Entropy, 19(9), 488. Multidisciplinary Digital Publishing Institute.
Laib, M., Golay, J., Telesca, L., & Kanevski, M. (2018). Multifractal analysis of the time series of daily means of wind speed in complex regions. Chaos, Solitons & Fractals, 109, 118–127. Elsevier.
Laib, M., Guignard, F., Kanevski, M., & Telesca, L. (2019). Community detection analysis in wind speed-monitoring systems using mutual information-based complex network. Chaos: An Interdisciplinary Journal of Nonlinear Science, 29(4), 43107. AIP Publishing LLC.
Li, H., Meng, Q., Wang, Y., & Zeng, M. (2011). Multi-scale entropy analysis of single-point wind speed in outdoor near-surface environments. 2011 International Conference on Electrical and Control Engineering (pp. 4579–4582). IEEE.
Li, Q., & Zuntao, F. (2014). Permutation entropy and statistical complexity quantifier of nonstationarity effect in the vertical velocity records. Physical Review E, 89(1), 12905. APS.
Ni, Q., Feng, K., Wang, K., Yang, B., & Wang, Y. (2017). A case study of sample entropy analysis to the fault detection of bearing in wind turbine. Case studies in engineering failure analysis, 9, 99–111. Elsevier.
Pereira, A. S., Shitsuka, D. M., Parreira, F. J., & Shitsuka, R. (2018). Metodologia da pesquisa científica Santa Maria, Brazil. https://repositorio.ufsm.br/bitstream/handle/1/15824/Lic_Computacao_Metodologia-Pesquisa-Cientifica.pdf?sequence=1
Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278(6), H2039–H2049. American Physiological Society Bethesda, MD.
Safari, B., & Gasore, J. (2010). A statistical investigation of wind characteristics and wind energy potential based on the Weibull and Rayleigh models in Rwanda. Renewable Energy, 35(12), 2874–2880. Elsevier.
Santana, L. V. R., Stosic, T., Ferreira, T. A. E., & Silva, A. S. A. da. (2020a). Comparação dos dados da velocidade do vento no Nordeste do Brasil da ERA-40 e Instituto Nacional de Meteorologia (INMET) utilizando medidas de entropia. Research, Society and Development, 9(8), e446985257. https://rsdjournal.org/index.php/rsd/article/view/5257
Santana, L. V. R., Stosic, T., Ferreira, T. A. E., & Silva, A. S. A. da. (2020b). Análise da regularidade da velocidade do vento no Nordeste do Brasil através da Sample Entropy. Research, Society and Development, 9(7), e762974746. https://rsdjournal.org/index.php/rsd/article/view/4746
Silva, B. B. da, Alves, J. J. A., Cavalcanti, E. P., & Dantas, R. T. (2002). Potencial eólico na direção predominante do vento no Nordeste brasileiro. Revista Brasileira de Engenharia Agrícola e Ambiental, 6(3), 431–439. SciELO Brasil.
Tar, K. (2008). Some statistical characteristics of monthly average wind speed at various heights. Renewable and Sustainable Energy Reviews, 12(6), 1712–1724. Elsevier.
Witzler, L. T., Ramos, D. S., Camargo, L. A. S., & Guarnier, E. (2016). Reconstruction of wind generation historical series aiming at the analysis of energy complementarity: Methodology and applications. 2016 13th International Conference on the European Energy Market (EEM) (pp. 1–6). IEEE.
Zhou, Y., Zhang, Q., Li, K., & Chen, X. (2012). Hydrological effects of water reservoirs on hydrological processes in the East River (China) basin: complexity evaluations based on the multi‐scale entropy analysis. Hydrological Processes, 26(21), 3253–3262. Wiley Online Library.
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