복잡지형에서 풍력자원 예측에 영향을 미치는 파라미터에 관한 연구

다국어 초록 (Multilingual Abstract)

In this study, six parameters affecting wind energy prediction in complex terrain including Ruggedness Index(RIX), wind speed, V, weibull shape parameter, k, wind shear exponent, α, wind direction, steepness.were investigated..

Firstly, we found a relationship between wind shear exponent, α, and a few factors such as the wind speed, V, ruggedness index(RIX), and the Weibull shape parameter, k of sites in complex terrain in Korea. Wind shear exponents in main wind directions were calculated using wind speed data measured for one year from various heights of eleven meteorological masts in Gangwon province. It was found from the analysis that the reciprocal of the wind shear exponent can be expressed by an exponentially decaying function with respect to a multiple of V, RIX and k. This result is considered useful to be used to characterize wind characteristics of specific sites in complex terrain in Korea with limited information.

Secondly, we performed a sensitivity analysis to see how the true north error of a wind vane installed to a meteorological mast affects predictions of the annual-average wind speed and the annual energy production. For this study, two meteorological masts were installed with a distance of about 4 km on the ridge in complex terrain and the wind speed and direction were measured for one year. Cross predictions of the wind speed and the AEP of a virtual wind turbine for two sites in complex terrain were performed by changing the wind direction from -45° to 45° with an interval of 5°. A commercial wind resource prediction program, WindPRO, was used for the study. It was found that the prediction errors in the AEP caused by the wind direction errors occurred up to more than 20% depending on the orography and the main wind direction at that site.

Thirdly, The annual energy production of Gangwon wind farm was predicted for three consecutive years of 2007, 2008 and 2009 using commercial programs, WindPRO and WindSim which are known to be used the most for wind resource prediction in the world. The predictions from the linear code, WindPRO, were compared with both the actual energy prediction presented in the CDM (Clean Development Mechanism) monitoring report of the wind farm and also the predictions from the CFD code, WindSim. The results from WindPRO were close to the actual energy productions and the errors were within 11.8 % unlike the expectation. The reason for the low prediction errors was found to be due to the fact that although the wind farm is located in highly complex terrain, the terrain steepness was smaller than the critical angle (21.8°) in front of the wind farm in the main wind direction. Therefore no flow separation was found to occur within the wind farm. The flow separation of the main wind was found to occur mostly behind the wind farm.

목차 (Table of Contents)

• 목 차 i
• List of Figures iii
• List of Tables v
• Ⅰ. 서론 1
• Ⅱ. 본론 3
• 1. 복잡지형에서의 풍력자원 평가 3
• 1.1 복잡지형에대한 설명 3
• 1.2 기상자료 7
• 1.3 기상계측타워(Meteorological Mast) 입지조건 10
• 2. 풍력자원 해석프로그램 13
• 2.1 WindPRO 13
• 2.2 WindSim 15
• 3. 복잡지형에서의 풍황특성 17
• 3.1 연구방법 17
• 3.2 실측지점 현황 17
• 3.3 Parameter 20
• 3.4 회귀분석 결과 29
• 4. 풍향의 변화가 풍력자원예측에 미치는 영향 31
• 4.1 연구방법 31
• 4.2 실측지점 현황 33
• 4.3 진북보정 35
• 4.4 실측데이터 검토 36
• 4.5 해석 결과 37
• 5. 지형의 경사도가 풍력자원예측에 미치는 영향 47
• 5.1 연구방법 47
• 5.2 풍력발전단지 현황 48
• 5.3 실측자료 49
• 5.4 해석 결과 51
• 5.5 토의 55
• Ⅲ. 결론 60
• List of Reference 62
• Abstract 65
• 감사의 글 67