Journal of the Korean Solar Energy Society (한국태양에너지학회 논문집)

The Korean Solar Energy Society (한국태양에너지학회)
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Variation of Capacity Factors by Weibull Shape Parameters

와이블 형상계수에 따른 이용률 변화

  • Kwon, Il-Han (Dept. of Mechanical and Mechatronics Engineering, Graduate School, Kangwon National University) ;
  • Kim, Jin-Han (Dept. of Mechanical and Mechatronics Engineering, Graduate School, Kangwon National University) ;
  • Paek, In-Su (Dept. of Mechanical and Mechatronics Engineering, Kangwon National University) ;
  • Yoo, Neung-Soo (Dept. of Mechanical and Mechatronics Engineering, Kangwon National University)
  • 권일한 (강원대학교 대학원 기계메카트로닉스공학과) ;
  • 김진한 (강원대학교 대학원 기계메카트로닉스공학과) ;
  • 백인수 (강원대학교 기계메카트로닉스공학과) ;
  • 유능수 (강원대학교 기계메카트로닉스공학과)
  • Received : 2012.09.13
  • Accepted : 2013.02.15
  • Published : 2013.02.28
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Abstract

Effects of Weibull shape parameter, k, on capacity factors of wind turbines were investigated. Wind distributions with mean wind speeds of 5 m/s, 6 m/s, 7 m/s and 8 m/s were simulated and used to estimate the annual energy productions and capacity factors of a 2MW wind turbine for various Weibull shape parameters. It was found from the study that the capacity factors of wind turbines are much affected by Weibull shape parameters. When the annual mean wind speed at the hub height of a wind turbine was about 7 m/s, and the air density was assumed to be 1.225 $kg/m^3$, the maximum capacity factor of a 2 MW wind turbine having a rated wind speed of 13 m/s was found to occur with the shape parameter of 2. It was also found that as the mean wind speed increased, the Weibull k parameter which yielded the maximum capacity factor increased. The simulated results were also validated by predictions of capacity factors of wind turbines using wind data measured in complex terrain.

Keywords

References

  1. Y. Hwang, I. Paek, K. Yoon, W. Lee, N. Yoo and Y. Nam, "Application of wind data from automated weather stations to wind resources estimation inKorea," Journal of Mechanical Science and Technology, Vol.24, pp.2017-2023, 2010. https://doi.org/10.1007/s12206-010-0613-z
  2. 김현기, 김병민, 백인수, 유능수, 김현구, "풍향의 변동성에 따른 연간에너지 발전량의 변화," 한국태양에너지학회 논문집, Vol.31, No.5, pp.1-8, 2011. https://doi.org/10.7836/kses.2011.31.5.001
  3. J. Woo, B. Kim, I. Paek, N. Yoo, and Y. Nam, "Investigation on selecting optimal wind turbines in the capacity factor pointofview," Journal of the Korean solarenergy society, Vol.31, No.5, pp. 60-66, 2011.
  4. J. Woo, H. Kim, I. Paek, N. Yoo, and Y. Nam, "Wind speedprediction in complex terrain using acommercial CFD code," Journal of the Korean solar energy society, Vol.31, No.6, pp.8-22, 2011.
  5. 우재균, 김현기, 김병민, 백인수, 유능수, "AWS 풍황데이터를 이용한 강원풍력발 전단지 연간에너지발전량 예측", 한국태양에너지학회 논문집,Vol.31,No.2, 2011.
  6. National Renewable Energy Laboratory, Wind Resource Assessment Handbook
  7. 황지욱, 유기표, 김한영, 기상데이터와 웨이블 파라메타를 이용한 풍력에너지밀도 분포 비교, 한국태양에너지학회 논문집, Vol.30, No.2, pp.54-64, 2011.
  8. "풍력공학입문, 기초이론부터 풍력발전기술까지", 2002, 고경남, 허종철 공역, 문운당 2006
  9. 고경남, 김경보, 허종철, 제주 서광지역에 대한 풍력에너지의 장기간 (10년)특성, 한국태양에너지학회 논문집, Vol28, No.3, pp.45-52, 2008.
  10. J.F. MANWELL, J.G. MCGOWAN, A.L.ROGERS, Wind Energy Explained Theory, Design and Application 2nd, Wiley, p 60, 2009.
  11. WindPRO Ver2.7 Manual, EMD.
  12. I. Troen and E.L. Petersen,, European Wind Atlas, Riso National Laboratory, p565,1989.