Stokastik sınır analizi kullanarak rüzgâr türbinleri için etkinlik değerlendirmesi

Yıl 2017, Cilt: 32 Sayı: 4, 1311 - 1326, 08.12.2017

Harika Akalın , Serap Ulusam Seçkiner , Yunus Eroğlu

https://doi.org/10.17341/gazimmfd.369740

Cited By: 2

Öz

Bu
çalışmada, Stokastik Sınır Analizi (SSA) tekniği, hâlihazırda işletmede olan
bir rüzgâr çiftliğinde üretim etkinliğini ölçmek için kullanılmıştır. Mevcut
literatürde çeşitli alanlarda yaygın olarak uygulanan bir etkinlik ölçüm
tekniği olan SSA, rüzgâr türbinlerinin etkinlik ölçümlerinde daha önce
kullanılmamıştır. SSA’nın stokastik yapısı, sınır sapmalarının hem işletmenin
kontrolünde olmayan dış etkileri hem de teknik etkinsizliği içermesi onun
anahtar özelliğidir. Önerilen yaklaşım, dört farklı senaryo altında her bir
türbinin birbirlerine göre etkinliklerini ve rüzgâr çiftliğinin tamamının
etkinliğini hesaplamak için kullanılmıştır. Bu senaryolar, çeşitli girdi
parametrelerinin etkinlik üzerindeki etkisini ölçmek için oluşturulmuştur.
Dolayısıyla, bu çalışmanın diğer bir amacı da farklı girdi senaryoları ile bir
rüzgâr türbininin etkinliğini ölçmek için hangi faktörlerin ne kadar etkili
olduğunu açıklamaktır. Ayrıca, bu dört senaryo, aylık ortalama veriler, on iki
aylık veriler ve yirmi dört aylık veriler olmak üzere üç farklı zaman
periyodunda çalışılmıştır. Çalışmanın sonucunda farklı girdi gruplarına göre
farklı etkinlik skorları elde edilmiştir. Etkinlik kayıplarının istatiksel
sapmadan mı yoksa etkinsizlikten mi kaynaklandığı SSA yönteminin avantajı
gereği yorumlanabilmiştir.

Anahtar Kelimeler

Rüzgâr türbini, performans değerlendirme, stokastik sınır analizi

Kaynakça

• 1. WWEA, The World Wind Energy Association (WWEA) Half Year Report 2014. http://www.wwindea.org/wwea-publishes-half-year-report-2014/. Yayın tarihi Eylül 2014.Erişirm tarihi Mart 29, 2016.
• 2. Da Rosa AV., Fundamentals of renewable energy processes, Elsevier, Amsterdam, 2013.
• 3. Eroğlu Y., Seçkiner S.U., Performance analysis in wind farms by data envelopment analysis and Malmquist Index approaches. Pamukkale Univ. J. Eng. Sci., 23(1), 45-54, 2017.
• 4. GWEC, Global Wind Report - Annual Market Update 2015. http://www.gwec.net/wp-content/uploads/vip/GWEC-Global-Wind-2015-Report_April-2016_22_04.pdf. Yayın tarihi Nisan 19 2014.Erişirm tarihi Nisan 21, 2016.
• 5. Turkish Wind Energy Association, Turkish Wind Energy Statistics Report. http://www.tureb.com.tr/files/bilgi_bankasi/turkiye_res_durumu/2016_turkiye_ruzgar_enerji_istatistik_raporu_ocak_2016.pdf. Yayın tarihi Ocak 2016.Erişirm tarihi Ağustos 9, 2016.
• 6. Onar S.Ç., Kılavuz T N., Risk Analysis of Wind Energy Investments in Turkey. Hum. Ecol. Risk Assess. Int. J., 21 (5), 1230–1245, 2015.
• 7. Onar S.C., Oztaysi B., Otay İ., Kahraman C., Multi-expert wind energy technology selection using interval-valued intuitionistic fuzzy sets. Energy, 90 (1), 274–285, 2015.
• 8. Yang W., Tavner P.J., Crabtree C.J., Feng Y., Qiu Y., Wind turbine condition monitoring, technical and commercial challenges. Wind Energy, 17, 673–693, 2014.
• 9. Eroğlu Y., Seçkiner S.U., Trend Topic Analysis for Wind Energy Researches, A Data Mining Approach Using Text Mining. J. Technol. Innov. Renew. Energy, 5, 44–58, 2016.
• 10. Denholm P., Kulcinski G L., Holloway T., Emissions and Energy Efficiency Assessment of Baseload Wind Energy Systems. Environ. Sci. Technol., 39, 1903–1911, 2005.
• 11. Mirecki A., Roboam X., and Richardeau F., Architecture Complexity and Energy Efficiency of Small Wind Turbines. IEEE Trans. Ind. Electron., 54 (1), 660–670, 2007.
• 12. Zhang H., Tolbert L M., Efficiency Impact of Silicon Carbide Power Electronics for Modern Wind Turbine Full Scale Frequency Converter. IEEE Trans. Ind. Electron., 58, 21–28, 2011.
• 13. De Prada Gil M., Gomis-Bellmunt O., Sumper A., Bergas-Jané J., Power generation efficiency analysis of offshore wind farms connected to a SLPC (single large power converter) operated with variable frequencies considering wake effects. Energy, 37, 455–468, 2012.
• 14. Najar F.A., Harmain G.A., Blade Design and Performance Analysis of Wind Turbine. In, International Conference on Global Scenario in Environment and Energy. International Journal of Chem. Tech Research, 1054–1061, 2013. 15. Jiang H., Li Y., Cheng Z., Performances of ideal wind turbine. Renew. Energy, 83, 658–662, 2015. 16. Chehouri A., Younes R., Ilinca A., Perron J., Review of performance optimization techniques applied to wind turbines. Appl. Energy, 142, 361–388, 2015.
• 17. El-Baz A.R., Youssef K., Mohamed M.H., Innovative improvement of a drag wind turbine performance. Renew. Energy, 86, 89–98, 2016.
• 18. Astolfi D., Castellani F., Garinei A., Terzi L., Data mining techniques for performance analysis of onshore wind farms. Appl. Energy, 148, 220–233, 2015.
• 19. Mengi O.Ö., Altaş İ.H., A different fuzzy decision making MPPT method for a micro power wind turbine, Journal of the Faculty of Engineering and Architecture of Gazi University, 30 (2), 197–206, 2015.
• 20. Bayrak M., Eric N., Küçüker A., Detection of mechanical unbalanced faults in wind turbines by using electrical measurements, Journal of the Faculty of Engineering and Architecture of Gazi University, 31 (3), 687–694,2016.
• 21. Devrim Y., Bilir L., Performance investigation of a wind turbine–solar photovoltaic panels–fuel cell hybrid system installed at İncek region – Ankara, Turkey, Energy Convers. Manag., 126, 759–766, 2016.
• 22. Uluyol Ö., Parthasarathy G., Foslien W., Kim K., Power Curve Analytic for Wind Turbine Performance Monitoring and Prognostics. In, Annual Conference of the Prognostics and Health Management Society, 1–8., 2011.
• 23. Carrillo C., Obando Montaño A.F., Cidrás J., Díaz-Dorado E., Review of power curve modelling for wind turbines. Renew. Sustain. Energy Rev., 21, 572–581, 2013.
• 24. Lydia M., Kumar S.S., Selvakumar A.I., Prem Kumar G.E., A comprehensive review on wind turbine power curve modeling techniques. Renew. Sustain. Energy Rev., 30, 452–460, 2014.
• 25. Milan P., Wächter M., Peinke J., Stochastic modeling and performance monitoring of wind farm power production. J. Renew. Sustain. Energy, 6, 33119, 2014.
• 26. Herp J., Poulsen U.V., Greiner M., Wind farm power optimization including flow variability. Renew. Energy, 81, 173–181, 2015.
• 27. Kusiak A., Verma A., Monitoring Wind Farms With Performance Curves. IEEE Trans. Sustain. Energy, 4, 192–199, 2013.
• 28. Verma A., Performance monitoring of wind turbines , a data-mining approach, PhD, University of Iowa, Iowa, 2012.
• 29. Wagner R., Antoniou I., Pedersen S.M., Courtney M.S., Jørgensen H.E., The influence of the wind speed profile on wind turbine performance measurements. Wind Energy, 12, 348–362, 2009.
• 30. Al-Hadhrami L.M., Performance evaluation of small wind turbines for off grid applications in Saudi Arabia. Energy Convers. Manag., 81, 19–29, 2014.
• 31. Barthelmie R.J., Jensen L.E., Evaluation of wind farm efficiency and wind turbine wakes at the Nysted offshore wind farm. Wind Energy, 13, 573–586, 2010.
• 32. Pieralli S., Ritter M., Odening M., Efficiency of wind power production and its determinants. Energy. 90, Part 1, 429–438, 2015.
• 33. Krokoszinski H.J., Efficiency and effectiveness of wind farms—keys to cost optimized operation and maintenance. Renew. Energy, 28, 2165–2178, 2003.
• 34. Bortolini M., Gamberi M., Graziani A., Manzini R., Pilati F., Performance and viability analysis of small wind turbines in the European Union. Renew. Energy, 62, 629–639, 2014.
• 35. Zhang Z., Performance optimization of wind turbines, Phd, University of Iowa, Iowa, 2012.
• 36. Eroğlu Y., Seçkiner S.U., Design of wind farm layout using ant colony algorithm. Renew. Energy, 44, 53–62, 2012.
• 37. Eroğlu Y., Seçkiner S.U., Wind farm layout optimization using particle filtering approach. Renew. Energy, 58, 95–107 ,2013.
• 38. Lo S.F., Wu C.Y., Evaluating the performance of wind farms in China, An empirical review. Int. J. Electr. Power Energy Syst., 69, 58–66, 2015.
• 39. Iglesias G., Castellanos P., Seijas A., Measurement of productive efficiency with frontier methods, A case study for wind farms. Energy Econ., 32, 1199–1208, 2010.
• 40. Aigner D., Lovell C.A.K., Schmidt P., Formulation and estimation of stochastic frontier production function models. J. Econom., 6, 21–37, 1977.
• 41. Meeusen W., van Den Broeck J., Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error. Int. Econ. Rev., 18, 435–444, 1977.
• 42. Zhou P., Ang B.W., Zhou D.Q., Measuring economy-wide energy efficiency performance, A parametric frontier approach. Appl. Energy, 90, 196–200, 2012.
• 43. Hu J.L., Honma S., A Comparative Study of Energy Efficiency of OECD Countries, An Application of the Stochastic Frontier Analysis. Energy Procedia, 61, 2280–2283, 2014.
• 44. Yu F.W., Chan K.T., Yang J., Sit R.K.Y., Comparative study on the energy performance of chiller system in an institutional building with stochastic frontier analysis. Energy Build., 89, 206–212, 2015.
• 45. Lin B., Long H., A stochastic frontier analysis of energy efficiency of China’s chemical industry. J. Clean. Prod., 87, 235–244, 2015.
• 46. Battese G.E., Coelli T.J., A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empir. Econ., 20, 325–332, 1995.
• 47. Battese G.E., Corra G.S., Estimation of a Production Frontier Model, With Application to the Pastoral Zone of Eastern Australia. Aust. J. Agric. Econ., 21, 169–179, 1977.
• 48. Coelli T.J., A guide to FRONTIER version 4.1, a computer program for stochastic frontier production and cost function estimation. CEPA Work. Pap. 7, 1–96, 1996.
• 49. Cordeiro J.J., Sarkis J., Vazquez-Brust D., Frater L., Dijkshoorn J., An evaluation of technical efficiency and managerial correlates of solid waste management by Welsh SMEs using parametric and non-parametric techniques. J. Oper. Res. Soc., 63, 653–664, 2012.
• 50. Kodde D.A., Palm F., Wald Criteria for Jointly Testing Equality and Inequality Restrictions. Econometrica, 54 (5), 1243-48, 1986.
• 51. Jacobs R., Smith P.C., Street A., Measuring Efficiency in Health Care, Analytic Techniques and Health Policy, Cambridge University Press, 2006.