와이블 형상계수에 따른 이용률 변화
Variation of Capacity Factors by Weibull Shape Parameters
0
조회
0
다운로드
6
인용
0
참고문헌
키워드
초록
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.
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.
참고문헌 (12)
- Yoonseok Hwang, "Application of wind data from automated weather stations to wind resources estimation in Korea", Journal of Mechanical Science and Technology, 24(10), pp.2017~2023 (2010), 대한기계학회
- 김현기, "풍향의 변동성에 따른 연간에너지 발전량의 변화", 한국태양에너지학회 논문집, 31(5), pp.1~8 (2011), 한국태양에너지학회
- 우재균, "Investigation on Selecting Optimal Wind Turbines in the Capacity Factor Point of View", 한국태양에너지학회 논문집, 31(5), pp.60~66 (2011), 한국태양에너지학회
- 우재균, "Wind Speed Prediction in Complex Terrain Using a Commercial CFD Code.", 한국태양에너지학회 논문집, 31(6), pp.8~22 (2011), 한국태양에너지학회
- 우재균, "AWS 풍황데이터를 이용한 강원풍력발전단지 연간에너지발전량 예측", 한국태양에너지학회 논문집, 31(2), pp.72~81 (2011), 한국태양에너지학회
- National Renewable Energy Laboratory, "Wind Resource Assessment Handbook", ~
- 황지욱, "기상데이터와 웨이블 파라메타를 이용한 풍력에너지밀도분포 비교", 한국태양에너지학회 논문집, 30(2), pp.54~64 (2010), 한국태양에너지학회
- 고경남, "풍력공학입문, 기초이론부터 풍력발전기술까지" (2006), 문운당
- 고경남, "제주 서광지역에 대한 풍력에너지의 장기간 (10년) 특성", 한국태양에너지학회 논문집, 28(3), pp.45~52 (2008), 한국태양에너지학회
- J.F. MANWELL, "Wind Energy Explained Theory, Design and Application", pp.60~ (2009), Wiley
- "WindPRO Ver 2.7 Manual", EMD, ~
- I. Troen, "European Wind Atlas", pp.565~ (1989), Riso National Laboratory