Araştırma Makalesi
BibTex RIS Kaynak Göster

Yıl 2026, Cilt: 11 Sayı: 1, 339 - 351, 17.03.2026
https://doi.org/10.58559/ijes.1845806
https://izlik.org/JA92CC69LT

Öz

Kaynakça

  • [1] Elliott DL, Holladay CG, Barchet WR, Foote HP, Sandusky WF. Wind energy resource atlas of the United States. Solar Energy Research Institute, Golden, CO, US, 1986.
  • [2] El-Osta W. Evaluation of wind energy potential in Libya. Applied Energy Special Process 1995.
  • [3] Mohamed AMA, Al-Habaibeh A, Abdo H. An investigation into the current utilisation and prospective of renewable energy resources and technologies in Libya. Nottingham Trent University, Nottingham, UK.
  • [4] Mohammed B, Milad M. Wind load characteristics in Libya. World Academy of Science, Engineering and Technology 2010; 63.
  • [5] Mathew S. Wind energy fundamentals, resource analysis and economics. Springer-Verlag, Berlin Heidelberg, Netherlands, 2006.
  • [6] Johnson GL. Wind energy systems. Manhattan, KS, US, 2006.
  • [7] Ayodele TR, Jimoh AA, Munda JL, Agee JT. Viability and economic analysis of wind energy resource for power generation in Johannesburg, South Africa. International Journal Of Sustainable Energy 2013; 33: 284–303.
  • [8] Irwanto M, Gomesh N, Mamat M, Yusoff Y. Assessment of wind power generation potential in Perlis, Malaysia. Renewable And Sustainable Energy Reviews 2014; 38: 296–308.
  • [9] Mehr G, Nengling T, Wentao H, Nadeem MH, Yu M. Assessment of wind power potential and economic analysis at Hyderabad in Pakistan: powering to local communities using wind power. Sustainability 2019; 11: 1391.
  • [10] Kassem Y, Gökçekuş H, AbuGharara MA. An investigation on wind energy potential in Nalut, Libya, using Weibull distribution. International Journal Of Applied Engineering Research 2019; 14(10): 2474–2482.
  • [11] Hogg RV, Klugman SA. Loss distributions. John Wiley & Sons, Inc., New York, US, 1984.
  • [12] Chaturvedi A, Malhotra A. Inference on the parameters and reliability characteristics of three parameter Burr distribution based on records. Applied Mathematics & Information Sciences 2017; 11(3): 837–849.
  • [13] EasyFit software help. MathWave Technologies, 2023. Available at: http://www.mathwaves.com
  • [14] Hussin NH, Yusof F. Analysis of wind speed characteristics using probability distribution in Johor. Environment And Ecology Research 2022; 10(1): 95–106.
  • [15] Alayat MM, Kassem Y, Çamur H. Assessment of wind energy potential as a power generation source: a case study of eight selected locations in Northern Cyprus. Energies 2018; 11: 2697.
  • [16] Huang S, Oluyede BO. Exponentiated Kumaraswamy-Dagum distribution with applications to income and lifetime data. Journal Of Statistical Distributions And Applications 2013; 1(8): 1–20.
  • [17] Obanla OJ, Awariefe C, Owolabi IH. On the investigation of the methods of parameter estimation of the best probability model for wind speed data. International Journal Of Discrete Mathematics 2021; 6(2): 45–51.

Wind energy estimation in Sabratha and Msallata: A comparison study

Yıl 2026, Cilt: 11 Sayı: 1, 339 - 351, 17.03.2026
https://doi.org/10.58559/ijes.1845806
https://izlik.org/JA92CC69LT

Öz

Accurate wind resource assessment is fundamental to developing wind power, a key contributor to a sustainable energy future. This study presents a comparative analysis of the wind potential in Sabratha and Msallata, Libya, using 2017-2018 meteorological data for Msallata and online data for Sabratha, then Wind speed data were processed and fitted to probability distributions using EasyFit software, which identified the Burr and Johnson SB distributions as the best fit, as determined by Kolmogorov-Smirnov and Anderson-Darling tests. Mean monthly wind speed at 10m height was 5–8 m/s for Msallata and 5–11 m/s for Sabratha, corresponding to a wind power density (WPD) of 76.92–289.1 W/m² and 97.08–390.81 W/m², respectively. This classifies both sites as having good-to-very good wind potential. A technical assessment of various wind turbines identified the Winwind-1MW and ENERCON-E53 models as the most efficient, achieving capacity factors exceeding 40%. These results demonstrate a highly productive synergy between the local wind regimes and specific turbine technologies, providing critical insights for future wind energy projects in western Libya.

Kaynakça

  • [1] Elliott DL, Holladay CG, Barchet WR, Foote HP, Sandusky WF. Wind energy resource atlas of the United States. Solar Energy Research Institute, Golden, CO, US, 1986.
  • [2] El-Osta W. Evaluation of wind energy potential in Libya. Applied Energy Special Process 1995.
  • [3] Mohamed AMA, Al-Habaibeh A, Abdo H. An investigation into the current utilisation and prospective of renewable energy resources and technologies in Libya. Nottingham Trent University, Nottingham, UK.
  • [4] Mohammed B, Milad M. Wind load characteristics in Libya. World Academy of Science, Engineering and Technology 2010; 63.
  • [5] Mathew S. Wind energy fundamentals, resource analysis and economics. Springer-Verlag, Berlin Heidelberg, Netherlands, 2006.
  • [6] Johnson GL. Wind energy systems. Manhattan, KS, US, 2006.
  • [7] Ayodele TR, Jimoh AA, Munda JL, Agee JT. Viability and economic analysis of wind energy resource for power generation in Johannesburg, South Africa. International Journal Of Sustainable Energy 2013; 33: 284–303.
  • [8] Irwanto M, Gomesh N, Mamat M, Yusoff Y. Assessment of wind power generation potential in Perlis, Malaysia. Renewable And Sustainable Energy Reviews 2014; 38: 296–308.
  • [9] Mehr G, Nengling T, Wentao H, Nadeem MH, Yu M. Assessment of wind power potential and economic analysis at Hyderabad in Pakistan: powering to local communities using wind power. Sustainability 2019; 11: 1391.
  • [10] Kassem Y, Gökçekuş H, AbuGharara MA. An investigation on wind energy potential in Nalut, Libya, using Weibull distribution. International Journal Of Applied Engineering Research 2019; 14(10): 2474–2482.
  • [11] Hogg RV, Klugman SA. Loss distributions. John Wiley & Sons, Inc., New York, US, 1984.
  • [12] Chaturvedi A, Malhotra A. Inference on the parameters and reliability characteristics of three parameter Burr distribution based on records. Applied Mathematics & Information Sciences 2017; 11(3): 837–849.
  • [13] EasyFit software help. MathWave Technologies, 2023. Available at: http://www.mathwaves.com
  • [14] Hussin NH, Yusof F. Analysis of wind speed characteristics using probability distribution in Johor. Environment And Ecology Research 2022; 10(1): 95–106.
  • [15] Alayat MM, Kassem Y, Çamur H. Assessment of wind energy potential as a power generation source: a case study of eight selected locations in Northern Cyprus. Energies 2018; 11: 2697.
  • [16] Huang S, Oluyede BO. Exponentiated Kumaraswamy-Dagum distribution with applications to income and lifetime data. Journal Of Statistical Distributions And Applications 2013; 1(8): 1–20.
  • [17] Obanla OJ, Awariefe C, Owolabi IH. On the investigation of the methods of parameter estimation of the best probability model for wind speed data. International Journal Of Discrete Mathematics 2021; 6(2): 45–51.
Toplam 17 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Rüzgar Enerjisi Sistemleri
Bölüm Araştırma Makalesi
Yazarlar

Rabee Ahmeed 0009-0003-8405-3600

Gönderilme Tarihi 20 Aralık 2025
Kabul Tarihi 20 Ocak 2026
Yayımlanma Tarihi 17 Mart 2026
DOI https://doi.org/10.58559/ijes.1845806
IZ https://izlik.org/JA92CC69LT
Yayımlandığı Sayı Yıl 2026 Cilt: 11 Sayı: 1

Kaynak Göster

APA Ahmeed, R. (2026). Wind energy estimation in Sabratha and Msallata: A comparison study. International Journal of Energy Studies, 11(1), 339-351. https://doi.org/10.58559/ijes.1845806
AMA 1.Ahmeed R. Wind energy estimation in Sabratha and Msallata: A comparison study. International Journal of Energy Studies. 2026;11(1):339-351. doi:10.58559/ijes.1845806
Chicago Ahmeed, Rabee. 2026. “Wind energy estimation in Sabratha and Msallata: A comparison study”. International Journal of Energy Studies 11 (1): 339-51. https://doi.org/10.58559/ijes.1845806.
EndNote Ahmeed R (01 Mart 2026) Wind energy estimation in Sabratha and Msallata: A comparison study. International Journal of Energy Studies 11 1 339–351.
IEEE [1]R. Ahmeed, “Wind energy estimation in Sabratha and Msallata: A comparison study”, International Journal of Energy Studies, c. 11, sy 1, ss. 339–351, Mar. 2026, doi: 10.58559/ijes.1845806.
ISNAD Ahmeed, Rabee. “Wind energy estimation in Sabratha and Msallata: A comparison study”. International Journal of Energy Studies 11/1 (01 Mart 2026): 339-351. https://doi.org/10.58559/ijes.1845806.
JAMA 1.Ahmeed R. Wind energy estimation in Sabratha and Msallata: A comparison study. International Journal of Energy Studies. 2026;11:339–351.
MLA Ahmeed, Rabee. “Wind energy estimation in Sabratha and Msallata: A comparison study”. International Journal of Energy Studies, c. 11, sy 1, Mart 2026, ss. 339-51, doi:10.58559/ijes.1845806.
Vancouver 1.Rabee Ahmeed. Wind energy estimation in Sabratha and Msallata: A comparison study. International Journal of Energy Studies. 01 Mart 2026;11(1):339-51. doi:10.58559/ijes.1845806