Performance assessment of Power Density Method for determining the Weibull Distribution Coefficients at three different locations
Tài liệu tham khảo
Kaplan, 2015, Overview of wind energy in the world and assesment of current wind energy policies in Turkey, Renew. Sustain. Energy Rev., 43, 562, 10.1016/j.rser.2014.11.027
Agalar, 2015, Design of a custom power park for wind turbine system and analysis of the system performance under power quality disturbances, IET Renew. Power Gener., 9, 943, 10.1049/iet-rpg.2014.0412
Çapika, 2012, Present situation and potential role of renewable energy in Turkey, Renew. Energy, 46, 1, 10.1016/j.renene.2012.02.031
Gabbasa, 2013, Review of the energy supply status for sustainable development in the Organization of Islamic Conference”, Renew. Sustain. Energy Rev., 28, 18, 10.1016/j.rser.2013.07.045
GWEC, (GlobalWindEnergyCouncil), Global wınd 2017 report, April, 2018.
Pishgar-Komleh, 2015, Wind speed and power density analysis based on Waybill and Rayleigh distributions (a case study: firouzkooh county of Iran), Renew. Sustain Energy Rev., 42, 313, 10.1016/j.rser.2014.10.028
Kantar, 2008, Analysis of wind speed distributions: wind distribution function derived from minimum cross entropy principles as better alternative to Weibull function, Energy Convers. Manag., 49, 962, 10.1016/j.enconman.2007.10.008
Akdağ, 1761, A new method to estimate Weibull parameters for wind energy applications, Energy Convers. Manag, 50, 2009
Khan, 2015, Determination of Weibull parameter by four Numerical methods and prediction of wind speed in Jiwani (Balochistan), J. Basic Appl. Sci., 11, 62, 10.6000/1927-5129.2015.11.08
Rocha, 2012, Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil, Appl. Energy, 89, 395, 10.1016/j.apenergy.2011.08.003
Freitas de Andrade, 2014, An efficiency comparison of numerical methods for determining Weibull parameters for wind energy applications: a new approach applied to the northeast region of Brazil, Energy Convers. Manag., 86, 801, 10.1016/j.enconman.2014.06.046
Azad, 2014, Statistical diagnosis of the best weibull methods for wind power assessment for agricultural applications, Energies, 7, 3056, 10.3390/en7053056
Kaoga, 2014, Performance assessment of two-parameter Weibull distribution methods for wind energy applications in the district of Maroua in Cameroon, Int. J. Sci. Basic Appl. Res. (IJSBAR), 17, 39
Islam, 2011, Assessment of wind energy potentiality at Kudat and Labuan, Malays. Using Weibull Distrib. Funct. Energy, 36, 985
Chang, 2011, Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application, Appl. Energy, 88, 272, 10.1016/j.apenergy.2010.06.018
Morgan, 2011, Probability distributions of offshore wind speeds, Energy Convers. Manag., 52, 15, 10.1016/j.enconman.2010.06.015
Mohammadi, 2013, Using different methods for comprehensive study of wind turbine utilization in Zarrineh, Iran, Energy Convers. Manag., 65, 463, 10.1016/j.enconman.2012.09.004
Gokcek, 2007, Investigation of wind characteristics and wind energy potential in Kirklareli, Turkey, Renew. Energy, 32, 1739, 10.1016/j.renene.2006.11.017
Talha, 2014, Comparative study of numerical methods for determining Weibull parameters for wind energy potential, Renew. Sustain. Energy Rev., 40, 820, 10.1016/j.rser.2014.08.009