[논문]측정-상관-예측법을 이용한 장기간 풍속 및 설비이용률의 예측

고경남; 허종철

doi:10.7836/kses.2012.32.6.037

측정-상관-예측법을 이용한 장기간 풍속 및 설비이용률의 예측
Prediction of long-term wind speed and capacity factor using Measure-Correlate-Predict method 원문보기

한국태양에너지학회 논문집 = Journal of the Korean Solar Energy Society, v.32 no.6, 2012년, pp.37 - 43

고경남 (제주대학교 대학원 풍력특성화협동과) , 허종철 (제주대학교 기계시스템공학부)

Abstract ▼ AI-Helper

Long-term variations in wind speed and capacity factor(CF) on Seongsan wind farm of Jeju Island, South Korea were derived statistically. The selected areas for this study were Subji, having a year wind data at 30m above ground level, Sinsan, having 30-year wind data at 10m above ground level and Seongsan wind farm, where long-term CF was predicted. The Measure-Correlate-Predict module of WindPRO was used to predict long-tem wind characteristics at Seongsan wind farm. Eachyear's CF was derived from the estimated 30-year time series wind data by running WAsP module. As a result, for the 30-year CFs, Seongsan wind farm was estimated to have 8.3% for the coefficien to fvariation, CV, and-16.5% ~ 13.2% for the range of variation, RV. It was predicted that the annual CF at Seongsan wind farm varied within about ${\pm}4%$.

주제어

AI 본문요약
AI-Helper

* AI 자동 식별 결과로 적합하지 않은 문장이 있을 수 있으니, 이용에 유의하시기 바랍니다.

문제 정의

This investigation aims to clarify the coefficient of variation and the range of variation for annual CF as well as annual wind speed at Seongsan wind farm.

제안 방법

The Regression and Matrix methods were recommended¹¹⁾ in case the wind data was available as high quality and detailed time series at both the measurement and the reference sites. In this study, since the wind data at Sinsan and Subji met the requirements, Regression and Matrix methods were applied to predict long-term wind data at Subji. The wind speed data predicted by means of Regression and Matrix methods was compared with the measured wind speed data.

대상 데이터

WindPRO software that was developed by EMD international A/S of Denmark was used for this study. Roughness map for indicating topographical conditions is presented in Figure 2.

이론/모형

In this investigation, the 30-year hourly wind data at Subji is predicted using MCP technique. Then each year’s CF at Seongsan is obtained by running WAsP module of WindPRO based on each year's the Wind Statistics at Subji.
In this study, the wind data for 30 years at Seongsan wind farm of Jeju Island were predicted using MCP method. This investigation aims to clarify the coefficient of variation and the range of variation for annual CF as well as annual wind speed at Seongsan wind farm.

성능/효과

(1)The CV and the RV for the 30-year CFs at Seongsan wind farm were estimated to be 8.3% and-16.5%∼13.2%, respectively.
(2) Seong san wind farm was estimated to be normal for inter-annual variation in wind speed and CF.
(3)It was estimated that the annual CF and the annual average wind speed for Seongsan wind farm varied within about ±4% and ±0.5m/s, respectively.

참고문헌 (13)

IEA Wind. IEA Wind 2010 Annual Report. 2011.
BTM Consult ApS. http://www.btm.dk.
Weisser D, Foxon TJ. Implications of seasonal and diurnal variations of wind velocity for power output estimation of a turbine:acasestudy of Grenada. International Journal of Energy Research, Vol.27, pp.1165-117, 2003.

상세보기
Sinden G. Characteristics of the UK wind resource: long-term patterns and relationship to electricity demand. Energy Policy, Vol. 35, pp.112-127, 2007.

상세보기
Yoreley CS, Jorge XB. Statistical analysis of wind power in the region of Veracruz (Mexico). Renewable Energy, Vol.34, pp. 1628-1634, 2009.

상세보기
Erich Hau. Wind Trubines - Fundamentals, Technologies, Application, Economics. Springer: Verlag Berlin Heidelberg, pp.464-468, 2006.
Ko KN, Kim KB, HuhJC. Variations of wind speed in time on Jeju Island, Korea. Energy, Vol.35, pp.3381-3387, 2010.

상세보기
Ko KN, Kim KB, Huh JC. Characteristics of wind variations on JejuI sland, Korea. International Journal of Energy Research, Vol.34, pp.36-45, 2010.

상세보기
EMD International A/S. WindPRO 2.5 User Guide. 2006.
Manwell JF, Mcgowan JG, Rogers AL. Wind energy explained. Wiley:p.28, 2010.
Thogersen ML, Motta M, Sorensen T, Nielsen P. Measure-Correlate-Predict method: casestudies and soft ware implementation. European Wind Energy Conference and Exhibition, Milan(IT), 7-10 May 2007.
Seo HS, Kyong NH, Kim HG. Analysis on wind characteristics for off shore wind structure turbine design atWaljeong, Jejudo. The Wind Engineering Institute of Korea, Vol.14, No.3, pp.161-167, 2010.
Nakao T, Sugiya T, Kato H, Kobayashi Y. Characteristics on the wind variability for the planning of wind turbine generation systems. Journal of the Japan Wind Energy Association, Vol.28, No.4, pp. 95-99, 2004.

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관 관리번호, 논문명, 대등논문명, 저자 , 저널/프로시딩명, 발행기관, 발행년, 발행언어, 권, 호, 시작페이지, 끝페이지, ISBN, ISSN, 주제분야, 키워드, 초록(한글), 초록(영문), 저자(소속기관)
저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

연합인증