Research Article
BibTex RIS Cite
Year 2020, Volume: 1 Issue: 1, 50 - 60, 03.11.2020

Abstract

References

  • [1] Kaplan Y.A., “Overview of Wind Energy in the World and Assessment of Current Wind Energy Policies in Turkey”, Renewable and Sustainable Energy Reviews, 43 C, pp. 562-568, (2015)
  • [2] Kaplan Y.A., San I., “Current Situation of Wind Energy in the World and Turkey, Green Energy Conference-VI” (IGEC-VI), Eskisehir, Turkey, (2011)
  • [3] Çapika M, Yılmaz AO., Çavusoglu I., “Present situation and potential role of renewable energy in Turkey”, Renewable Energy; 46, pp: 1-13, (2012)
  • [4] Gabbasa M, Sopian K, Yaakob Z, Zonooz M, Fudholi A, Asim N., “Review of the energy supply status for sustainable development in the Organization of Islamic Conference” Renewable and Sustainable Energy Reviews;28 pp:18–28, (2013)
  • [5] Mohammadi, Kasra, et al. Assessing different parameters estimation methods of Weibull distribution to compute wind power density. Energy Conversion and Management, 2016, 108: 322-335.
  • [6] Kantar Y.M., Usta I., “Analysis of wind speed distributions: wind distribution function derived from minimum cross entropy principles as better alternative to Weibull function”, Energy Convers Manage, 49, pp. 962–973 (2008)
  • [7] Akdağ S.A., Dinler A., “A new method to estimate Weibull parameters for wind energy applications”, Energy Convers Manag, 50, pp 1761-1766 (2009)
  • [8] Bilgili M. ve Şahin B., “The finding of weibull parameters at the determination of Wind Power density”, New and Renewable Energy / Energy Management Symposium, Kayseri, 229-234, (2005)
  • [9] Rocha P. A. C. R. Sousa R. C. D. Andrade C. F.D, and Silva M. E. V. D. “Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil”, Applied Energy, 89, pp 395–400, (2012)
  • [10] Freitas de Andrade C., Maia Neto H. F., Costa Rocha P. A., Vieira da Silva M. E., “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 Manage, 86 (10) pp. 801–808 (2014)
  • [11] Chang T. P., “Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application”. Appl Energy, 88, pp. 272–282 (2011)
  • [12] Bilir L. İmir M. Devrim Y. Albostan A. “Seasonal and yearly wind speed distribution and wind power density analysis based on Weibull distribution function” , IntJ.Hydrogen Energy http://dx.doi.org/10.1016/j.ijhydene.2015.04.140, (2015)
  • [13] Bidaoui, Hicham, et al. Wind speed data analysis using Weibull and Rayleigh distribution functions, case study: five cities northern Morocco. Procedia Manufacturing, 2019, 32: 786-793.
  • [14] Azhar, Noreen, et al. Wind Data Analysis of Coastal Region of Balochistan (Pakistan) by Weibull and Rayleigh Method. Indian Journal of Science and Technology, 2019, 12: 26.
  • [15] Sumair, Muhammad, et al. Application of five continuous distributions and evaluation of wind potential at five stations using normal distribution. Energy Exploration & Exploitation, 2020, 0144598720939373.
  • [16] Akpinar, Ebru Kavak. Statistical Analysis of Wind Speed Distribution With Sinop-Turkey Application. Journal of Thermal Engineering, 2019, 5.4: 277-292.
  • [17] Shoaib, Muhammad, et al. Assessment of wind energy potential using wind energy conversion system. Journal of cleaner production, 2019, 216: 346-360.
  • [18] Maheshwari M., Chandrasekaran RS., Babu D., “Optimization of Electrical Power Using Solar and Wind Energy Systems”, Proceedings of7'h International Conference on Intelligent Systems and Control (ISCO 2013), (2012)
  • [19] GWEC (Global Wind Energy Council), Global Wınd 2019 Report
  • [20] EWEA (European Wind Energy Association), Wind in power 2010 European statistics, February 2011
  • [21] Simsek HA, Simsek N., “Recent incentives for renewable energy in Turkey”, Energy Policy;63, pp: 521–530 (2013)
  • [22] Baris K, Kucukali S., “Availibility of renewable energy sources in Turkey: Current situation, potential, government policies and the EU perspective”, Energy Policy, 42, pp: 377–391, (2012)
  • [23] Caglar, Ahmet. Investigation of Wind Characteristics for Antalya Region by Using Weibull Distribution. Cumhuriyet Science Journal, 2017, 38.4: 156-164.
  • [24] Azad A.K., Rasul M.G., Alam M.M., Uddin S.M.A., Mondal S.K., “Analysis of Wind Energy Conversion System Using Weibull Distribution”, Procedia Engineering., 90, pp. 725–732, (2014)
  • [25] Kım.J.-Yum.B., “Selection between Weibull and Lognormal Distributions: A Comparative Simulation Study” Computational Statistics&Data Analysis, 53(2), pp: 477-485, (2008)
  • [26] Kose R., Arif M.O., Erbas O., Tugcu A.., “The analysis of wind data and wind energy potential in Kutahya, Turkey”, Renewable and Sustainable Energy Reviews, 8, pp. 277–288, (2004)
  • [27] Yıldırım U., Gazibey Y., Güngör A., “Wind Energy Potential of Niğde”, Journal of Niğde University, 1(2), pp: 37-47, (2012)
  • [28] Ahmet Shata SA, Hanitsch R. “Evaluation of wind energy potential and electricity generation on the coast of Mediterranean Sea in Egypt”. Renew Energy, 31, pp: 1183–202, (2006)
  • [29] Gokcek M., Bayulken A., Bekdemir S., “Investigation of wind characteristics and wind energy potential in Kirklareli, Turkey”, Renewable Energy, 32, pp: 1739–1752, (2007)
  • [30] Zhou W, Yang H, Fang Z. “Wind power potential and characteristics analysis of the Pearl River Delta Region.” Renew Energy, 31, pp: 739–53, (2006)
  • [31] Celik A.N., “A statistical analysis of wind power density based on the Weibull and Rayleigh models at southern region of Turkey”, Renewable Energy, 29, pp. 593–604, (2003)
  • [32] Sabzpooshani M, Mohammadi K. “Establishing new empirical models for predicting monthly mean horizontal diffuse solar radiation in city of Isfahan, Iran” Energy; 69(5), pp 71‒77 (2014)

A COMPARISON OF WEIBULL AND RAYLEIGH DISTRIBUTION FUNCTIONS WITH MOMENT METHOD: A CASE STUDY OF OSMANİYE REGION IN TURKEY

Year 2020, Volume: 1 Issue: 1, 50 - 60, 03.11.2020

Abstract

In this study, wind energy potential of Osmaniye region is assessed statistically by using the hourly wind speed data of Turkish State Meteorological Service between 2009 and 2013. This study introduced the evaluation of Weibull Distribution Function and Rayleigh Distribution Function for Osmaniye Region. Weibull function is utilized for the situations such as there exist no information about wind speed distribution measurement or frequency distribution. Moment Method is used to calculate the Weibull and Rayleigh parameters. Moment Method, Weibull and Rayleigh Distribution Functions are explained in detail in this paper. Additionally, the Relative Percentage Error (RPE) statistical test is used to compare the efficiency of these distribution functions. The calculated power density of all used distribution function is a major key issue for suitability of wind energy. The evaluation of parameters and wind power distribution have a crucial role in producing electricity from wind power. The calculated power densities of all used distribution functions were compared with wind power density derived from measured wind data. This paper reveals the effectiveness of Weibull and Rayleigh Distribution Functions by using Moment Method for Osmaniye region of Turkey.

References

  • [1] Kaplan Y.A., “Overview of Wind Energy in the World and Assessment of Current Wind Energy Policies in Turkey”, Renewable and Sustainable Energy Reviews, 43 C, pp. 562-568, (2015)
  • [2] Kaplan Y.A., San I., “Current Situation of Wind Energy in the World and Turkey, Green Energy Conference-VI” (IGEC-VI), Eskisehir, Turkey, (2011)
  • [3] Çapika M, Yılmaz AO., Çavusoglu I., “Present situation and potential role of renewable energy in Turkey”, Renewable Energy; 46, pp: 1-13, (2012)
  • [4] Gabbasa M, Sopian K, Yaakob Z, Zonooz M, Fudholi A, Asim N., “Review of the energy supply status for sustainable development in the Organization of Islamic Conference” Renewable and Sustainable Energy Reviews;28 pp:18–28, (2013)
  • [5] Mohammadi, Kasra, et al. Assessing different parameters estimation methods of Weibull distribution to compute wind power density. Energy Conversion and Management, 2016, 108: 322-335.
  • [6] Kantar Y.M., Usta I., “Analysis of wind speed distributions: wind distribution function derived from minimum cross entropy principles as better alternative to Weibull function”, Energy Convers Manage, 49, pp. 962–973 (2008)
  • [7] Akdağ S.A., Dinler A., “A new method to estimate Weibull parameters for wind energy applications”, Energy Convers Manag, 50, pp 1761-1766 (2009)
  • [8] Bilgili M. ve Şahin B., “The finding of weibull parameters at the determination of Wind Power density”, New and Renewable Energy / Energy Management Symposium, Kayseri, 229-234, (2005)
  • [9] Rocha P. A. C. R. Sousa R. C. D. Andrade C. F.D, and Silva M. E. V. D. “Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil”, Applied Energy, 89, pp 395–400, (2012)
  • [10] Freitas de Andrade C., Maia Neto H. F., Costa Rocha P. A., Vieira da Silva M. E., “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 Manage, 86 (10) pp. 801–808 (2014)
  • [11] Chang T. P., “Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application”. Appl Energy, 88, pp. 272–282 (2011)
  • [12] Bilir L. İmir M. Devrim Y. Albostan A. “Seasonal and yearly wind speed distribution and wind power density analysis based on Weibull distribution function” , IntJ.Hydrogen Energy http://dx.doi.org/10.1016/j.ijhydene.2015.04.140, (2015)
  • [13] Bidaoui, Hicham, et al. Wind speed data analysis using Weibull and Rayleigh distribution functions, case study: five cities northern Morocco. Procedia Manufacturing, 2019, 32: 786-793.
  • [14] Azhar, Noreen, et al. Wind Data Analysis of Coastal Region of Balochistan (Pakistan) by Weibull and Rayleigh Method. Indian Journal of Science and Technology, 2019, 12: 26.
  • [15] Sumair, Muhammad, et al. Application of five continuous distributions and evaluation of wind potential at five stations using normal distribution. Energy Exploration & Exploitation, 2020, 0144598720939373.
  • [16] Akpinar, Ebru Kavak. Statistical Analysis of Wind Speed Distribution With Sinop-Turkey Application. Journal of Thermal Engineering, 2019, 5.4: 277-292.
  • [17] Shoaib, Muhammad, et al. Assessment of wind energy potential using wind energy conversion system. Journal of cleaner production, 2019, 216: 346-360.
  • [18] Maheshwari M., Chandrasekaran RS., Babu D., “Optimization of Electrical Power Using Solar and Wind Energy Systems”, Proceedings of7'h International Conference on Intelligent Systems and Control (ISCO 2013), (2012)
  • [19] GWEC (Global Wind Energy Council), Global Wınd 2019 Report
  • [20] EWEA (European Wind Energy Association), Wind in power 2010 European statistics, February 2011
  • [21] Simsek HA, Simsek N., “Recent incentives for renewable energy in Turkey”, Energy Policy;63, pp: 521–530 (2013)
  • [22] Baris K, Kucukali S., “Availibility of renewable energy sources in Turkey: Current situation, potential, government policies and the EU perspective”, Energy Policy, 42, pp: 377–391, (2012)
  • [23] Caglar, Ahmet. Investigation of Wind Characteristics for Antalya Region by Using Weibull Distribution. Cumhuriyet Science Journal, 2017, 38.4: 156-164.
  • [24] Azad A.K., Rasul M.G., Alam M.M., Uddin S.M.A., Mondal S.K., “Analysis of Wind Energy Conversion System Using Weibull Distribution”, Procedia Engineering., 90, pp. 725–732, (2014)
  • [25] Kım.J.-Yum.B., “Selection between Weibull and Lognormal Distributions: A Comparative Simulation Study” Computational Statistics&Data Analysis, 53(2), pp: 477-485, (2008)
  • [26] Kose R., Arif M.O., Erbas O., Tugcu A.., “The analysis of wind data and wind energy potential in Kutahya, Turkey”, Renewable and Sustainable Energy Reviews, 8, pp. 277–288, (2004)
  • [27] Yıldırım U., Gazibey Y., Güngör A., “Wind Energy Potential of Niğde”, Journal of Niğde University, 1(2), pp: 37-47, (2012)
  • [28] Ahmet Shata SA, Hanitsch R. “Evaluation of wind energy potential and electricity generation on the coast of Mediterranean Sea in Egypt”. Renew Energy, 31, pp: 1183–202, (2006)
  • [29] Gokcek M., Bayulken A., Bekdemir S., “Investigation of wind characteristics and wind energy potential in Kirklareli, Turkey”, Renewable Energy, 32, pp: 1739–1752, (2007)
  • [30] Zhou W, Yang H, Fang Z. “Wind power potential and characteristics analysis of the Pearl River Delta Region.” Renew Energy, 31, pp: 739–53, (2006)
  • [31] Celik A.N., “A statistical analysis of wind power density based on the Weibull and Rayleigh models at southern region of Turkey”, Renewable Energy, 29, pp. 593–604, (2003)
  • [32] Sabzpooshani M, Mohammadi K. “Establishing new empirical models for predicting monthly mean horizontal diffuse solar radiation in city of Isfahan, Iran” Energy; 69(5), pp 71‒77 (2014)
There are 32 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Yusuf Kaplan 0000-0003-1067-110X

Gülizar Gizem Ünaldı

Publication Date November 3, 2020
Published in Issue Year 2020 Volume: 1 Issue: 1

Cite

APA Kaplan, Y., & Ünaldı, G. G. (2020). A COMPARISON OF WEIBULL AND RAYLEIGH DISTRIBUTION FUNCTIONS WITH MOMENT METHOD: A CASE STUDY OF OSMANİYE REGION IN TURKEY. Eurasian Journal of Science Engineering and Technology, 1(1), 50-60.