Çekirdek yoğunluk kestirimi yönteminin rüzgâr türbini performans testinde kullanılması

Yıl 2020, Cilt: 26 Sayı: 5, 899 - 907, 23.10.2020

Ceyhun Yıldız Mustafa Şekkeli

Öz

Rüzgar Enerjisi Santrallarında (RES) gerçekleştirilen performans testleri, santral işletmecileri ve türbin üreticileri için oldukça önemlidir. Performans testlerinde enerji üretim hesabı için rüzgar hızı dağılımlarının modellenmesi gerekmektedir. Bu çalışmada, performans testlerinde kullanılan rüzgar hız dağılım modellerinin Çekirdek Yoğunluk Kestirimi (ÇYK) yöntemi ile oluşturulması önerilmiştir. Önerilen yöntemin uygulaması Türkiye enterkonnekte elektrik şebekesine bağlı bir RES’e ait rüzgar türbini (RT) üzerinde gerçekleştirilmiştir. Uygulama sonucu elde edilen sonuçlar, literatürce kabul görmüş iki farklı yöntem ile kıyaslanarak değerlendirilmiştir. Kıyaslanan yöntemlerden ilki International Electrotechnical Commission (IEC) 61400-12-2 No.lu Standartta önerilen Rayleigh fonksiyonu ile dağılım modelleme yöntemidir. İkincisi RES fizibilite çalışmalarında tercih edilen Weibull fonksiyonu ile dağılım modelleme yöntemidir. Değerlendirme sonucunda ÇYK, Rayleigh ve Weibull fonksiyonları kullanan yöntemlerin sırasıyla %1.32, %4.36 ve %12.12 mutlak hata ile bir yıllık üretimi hesaplayabildiği görülmüştür. Bu durum önerilen yöntemin literatürce kabul görmüş ve çok yaygın olarak kullanılan iki yöntemden daha doğru sonuç verdiğini göstermektedir. Önerilen yöntemin bir diğer avantajı ise non-parametrik olması ve parametrik Weibull, Rayleigh fonksiyonu kullanan yöntemlerde olduğu gibi parametre hesabına ihtiyaç duymamasıdır.

Anahtar Kelimeler

Rüzgâr türbini, Performans testi, Çekirdek yoğunluk kestirimi

Kaynakça

• [1] Celik AN. “A statistical analysis of wind power density based on the weibull and rayleigh models at the southern region of Turkey”. Renewable Energy, (29)4, 593-604, 2004.
• [2] Ucar A, Balo F. “Evaluation of wind energy potential and electricity generation at six locations in Turkey”. Applied Energy, 86(10), 1864-1872, 2009.
• [3] Bekele G, Palm B. “Wind energy potential assessment at four typical locations in Ethiopia”. Applied Energy, 86(3), 388-396, 2009.
• [4] Chang TP. “Performance comparison of six numerical methods in estimating weibull parameters for wind energy application”. Applied Energy, 88(1), 272-282, 2011.
• [5] Garcia A, Torres J, Prieto E, Francisco AD. “Fitting wind speed distributions: a case study”. Solar Energy, 62(2), 139-144, 1998.
• [6] Balouktsis A, Chassapis D, Karapantsios TD. “A nomogram method for estimating the energy produced by wind turbine generators”. Solar Energy, 72(3), 251-259, 2002.
• [7] Seguro J, Lambert T. “Modern estimation of the parameters of the weibull wind speed distribution for wind energy analysis”. Journal of Wind Engineering and Industrial Aerodynamics, 85(1), 75-84, 2000.
• [8] Burton T, Sharpe D, Jenkins N. Handbook Of Wind Energy. 1st ed. West Sussex, England, John Wiley & Sons, 2001.
• [9] Manwell JF, McGowan JG, Rogers AL. Wind Energy Explained: Theory, Design and Application. 2nd ed. West Sussex, England, John Wiley & Sons, 2010.
• [10] Carta JA, Ramirez P, Velazquez S. “A review of wind speed probability distributions used in wind energy analysis: case studies in the Canary Islands”. Renewable and Sustainable Energy Reviews, 13(5), 933-955, 2009.
• [11] Dorvlo AS. “Estimating wind speed distribution”. Energy Conversion and Management, 43(17), 2311-2318, 2002.
• [12] Morgan EC, Lackner M, Vogel RM, Baise LG. “probability distributions for offshore wind speeds”. Energy Conversion and Management, 52(1), 15-26, 2011.
• [13] Chiu ST, “A comparative review of bandwidth selection for kernel density estimation”. Statistica Sinica, 6(1), 129-145, 1996.
• [14] Turlach BA. "Bandwidth selection in kernel density estimation: a review". Working Paper, Institut fur Statistik und Okonometrie, Humboldt-Universitat zu Berlin, 1993.
• [15] Hu B, Li Y, Yang H, Wang H. “Wind speed model based on kernel density estimation and its application in reliability assessment of generating systems”. Journal of Modern Power Systems and Clean Energy, 5(2), 220-227, 2016.
• [16] Xu X, Yan Z, Xu S. “Estimating wind speed probability distribution by diffusion-based kernel density method”. Electric Power Systems Research, 121, 28-37, 2015.
• [17] Jeon J, Taylor JW. “Using conditional kernel density estimation for wind power density forecasting”. Journal of the American Statistical Association, 107(497), 66-79, 2012.
• [18] Miao S, Xie K, Yang H, Karki R, Tai HM, Chen T. “A mixture kernel density model for wind speed probability distribution estimation”. Energy Conversion and Management, 126, 1066-1083, 2016.
• [19] Zhang J, Chowdhury S, Messac A, Castillo L. “Multivariate and multimodal wind distribution model based on kernel density estimation”. ASME 2011 5th International Conference on Energy Sustainability, Washington, DC, USA August 7-10, 2011.
• [20] Yang N, Huang Y, Hou D, Liu S, Ye D, Dong B, Fan Y. “Adaptive nonparametric kernel density estimation approach for joint probability density function modeling of multiple wind farms”. Energies, 12(7), 1356, 2019.
• [21] Hu B, Li Y, Yang H, Wang H. “Wind speed model based on kernel density estimation and its application in reliability assessment of generating systems”. Journal of Modern Power System and Clean Energy, 5(2), 220-227, 2017.
• [22] Wang J, Hu J, Ma K. “Wind speed probability distribution estimation and wind energy assessment”. Renewable and Sustainable Energy Reviews, 60, 881-899, 2016.
• [23] Samal RK, Tripathy M. “Estimating wind speed probability distribution based on measured data at Burla in Odisha, India”. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 41(8), 918-930, 2019.
• [24] International Electro-technical Commission. "IEC 61400-12-2: Power performance of electricity producing wind turbines based on nacelle anemometry". 2012.
• [25] Mittelmeier N, Blodau T, Kühn M. “Monitoring offshore wind farm power performance with SCADA data and an advanced wake model”. Wind Energy Science, 2(1), 175-187, 2017.
• [26] Krishna VB, Ormel F, Hansen KS. “Alternative approach for establishing the nacelle transfer function”. Wind Engineering, 40(4), 307-318, 2016.
• [27] Hernandez W, López-Presa J, Maldonado-Correa J. “Power performance verification of a wind farm using the friedman’s test”. Sensors, 16(6) 816, 2016.
• [28] Haemi-Nasab MG, Franchini S, Davari AR, Sorribes-Palmer F. “A procedure for calibrating the spinning ultrasonic wind sensors”. Measurement, 114, 365-371, 2018.
• [29] Bingöl F. “Rüzgar enerji sistemleri için hava yoğunluğunun hesaplanması”. Journal of Polytechnic, 21(2), 273-281, 2018.
• [30] Yildiz C, Tekin M, Gani A, Kececioglu OF, Acikgoz H, Sekkeli M. “Considering air density effect on modelling wind farm power curve using site measurements”. PressAcademia Procedia, 5(1), 420-430, 2017.
• [31] Akdağ SA, Dinler A. “A new method to estimate weibull parameters for wind energy applications”. Energy Conversion and Management, 50(7), 1761-1766, 2009.
• [32] Safari B, Gasore J. “A statistical ınvestigation of wind characteristics and wind energy potential based on the weibull and rayleigh models in Rwanda”. Renewable Energy, 35(12), 2874-2880, 2010.
• [33] Kundu D, Raqab MZ. “Generalized rayleigh distribution: different methods of estimations”. Computational Statistics & Data Analysis, 49(1), 187-200, 2005.
• [34] Johnson NL, Kotz S, Balakrishnan N. Continuous Univariate Distributions. 2nd Ed. West Sussex, England, John Wiley & Sons, 2000.