Estimation of weibull parameters in winds speed mixture using nonlinear optimization for wind energy applications

被引:0
作者
Arrabal-Campos F.M. [1 ]
Montoya F.G. [1 ]
Alcayde A. [1 ]
Baños R. [1 ]
Martínez-Lao J. [1 ]
机构
[1] Department of Engineering E.S.I., University of Almeria, Carretera del sacramento s/n – La Cañada, Almería
来源
| 1600年 / European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ)卷 / 18期
关键词
Inversion winds speed mixture; Weibull distribution; Wind energy; Wind speed mixture;
D O I
10.24084/repqj18.327
中图分类号
学科分类号
摘要
Climate change and global warming are problems need to be tackled on a priority basis. The greenhouse gas (GHG) emissions and air pollution must be reduced by 25% and 40% compared to 1990 levels in 2020 and a reduction between 80% and 95% by 2050. To mitigate the GHG emissions, countries have adopted policies to use renewable energy sources. In the case of wind energy, the statistical analysis of wind data is a crucial stage for estimating the wind turbine energy output through the turbine performance. The Weibull distribution has been widely used in the recent years for describing the behavior of the wind speed and it can be treated as a probability density function. Herein, it is presented a new method for calculating the Weibull parameters of an infinity sum of Weibull distributions. This new method is based on a Hilbert space generated by scale and form factor as Fredholm integral. This new method is named Inversion of the Weibull Distribution in wind speed mixture (IWeD). The simulations results indicate that IWeD is adequate for estimating the Weibull parameters when the wind speed is composed of several Weibull distributions. © European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.
引用
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页码:351 / 355
页数:4
相关论文
共 32 条
  • [1] Zapata-Sierra A.J., Cama-Pinto A., Montoya F.G., Alcayde A., Manzano-Agugliaro F., Wind missing data arrangement using wavelet-based techniques for getting maximum likelihood, Energy Conversion and Management, 185, pp. 552-561, (2019)
  • [2] Cama A., Montoya F.G., Gomez J., De La Cruz J.L., Manzano-Agugliaro F., Integration of communication technologies in sensor networks to monitor the Amazon environment, Journal of Cleaner Production, 59, pp. 32-42, (2013)
  • [3] Al-mulali U., Factors affecting CO2 emission in the Middle East: A panel data analysis, Energy, 44, 1, pp. 564-569, (2012)
  • [4] Al-mulali U., Oil consumption, CO2 emission and economic growth in MENA countries, Energy, 36, 10, pp. 6165-6171, (2011)
  • [5] Sozen A., Gulseven Z., Arcaklioglu E., Forecasting based on sectoral energy consumption of GHGs in Turkey and mitigation policies, Energy Policy, 35, 12, pp. 6491-6505, (2007)
  • [6] Kawase R., Matsuoka Y., Fujino J., Decomposition analysis of CO2 emission in long-term climate stabilization scenarios, Energy Policy, 34, 15, pp. 2113-2122, (2006)
  • [7] Lu X., McElroy M. B., Global Potential for Wind-Generated Electricity, Wind Energy Engineering, 106, 27, pp. 51-73, (2017)
  • [8] Wind turbines generate more than 1% of the global electricity, (2019)
  • [9] Mostafaeipour A., Bardel B., Mohammadi K., Sedaghat A., Dinpashoh Y., Economic evaluation for cooling and ventilation of medicine storage warehouses utilizing wind catchers, Renewable and Sustainable Energy Reviews, 38, pp. 12-19, (2014)
  • [10] Mathew S., Philip G.S., Lim C.M., Analysis of Wind Regimes and Performance of Wind Turbines, (2011)