해상풍력 구조물 설계를 위한 제주도 월정지역 풍황특성 분석

서현수,경남호,김현구 한국풍공학회 2010 한국풍공학회지 Vol.14 No.3

The long-term wind data were regenerated from short-term met-tower data using a Measure-Correlate-Predict (MCP) method to prepare a design dataset for the 4MW offshore wind farm, which is being constructed at the Waljeong sea front, Jejudo. Two reference sites - the Jeju and Gujwa observation stations - and two MCP models - linear regression and matrix MCP - were tested. The results of a quantitative error analysis showed that Gujwa and the matrix MCP gave the best combination for long-term correction. Normal Turbulence Model (NTM) and Extreme Wind Model (EWM) analyses were performed with the regenerated Waljeong wind data to determine the class of the wind turbine type. According to the recommendation of the DNV Offshore Standard, the characteristic wind turbulence is estimated by the ninety-percent percentile of the standard deviation in the probability distribution. The 10-minute extreme wind speed of a 50 year return period was estimated using a Gumbel distribution of 8.5 year reconstructed Waljeong data. Therefore, the wind turbine type was found to be class II A. 제주도 월정 앞바다에서 건설 중인 4MW 해상풍력단지의 설계자료를 준비하기 위하여 측정-상관-예측(MCP) 방법을 이용하여 단기간 풍황탑 자료로부터 장기간 풍황자료를 재생산하였다. 두 참조지점으로서 제주, 구좌 기상관측소와 선형회귀 MCP, 행렬 MCP 조합에 대한 정량적 오차분석을 실시한 결과 구좌와 행렬 MCP에 의한 장기간 보정이 가장 최선의 조합임을 도출하였다. 재생산된 풍황자료를 이용하여 월정해상 풍력발전기의 형식등급을 결정하기위한 표준난류모델 및 극한풍속모델 해석을 수행하였다. 50년 회귀주기의 10분-단위 극한풍속을 예측하기 위해 재생산된 8.5년의 월정 풍황을 검블 분포로 해석하였다. 이에 의하여 결정된 풍력발전기 형식등급은 II A인 것으로 나타났다.

Prediction of long-term wind speed and capacity factor using Measure-Correlate-Predict method

Ko, Kyung-Nam(고경남),Huh, Jong-Chul(허종철) 한국태양에너지학회 2012 한국태양에너지학회 논문집 Vol.32 No.6

Long-term variations in wind speed and capacity factor(CF)on Seongsan wind farm of Jeju Island, South Korea were derived statistically. These lected areas for this study were Subji, having a year wind data at 30 m above ground level, Sinsan, having 30-year wind data at 10 m 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. Each year’s CF was derived from the estimated 30-year time series wind data by running WAsP module. As are sult, for the 30-year CFs, Seongsan wind farm was estimated to have 8.3% for the coefficient of variation, CV, and-16.5% ∼ 13.2% for the range of variati on, RV. It was predicted that the annual CF at Seongsan wind farm varied within about ±4%.

한국주식시장에서 유동성 측정치 비교

양철원 한국재무학회 2012 財務硏究 Vol.25 No.1

MCP방법을 이용한 장기간 풍속 및 풍력에너지 변동 특성 분석

현승건(Hyun Seung-Gun),장문석(Jang Moon-Seok),고석환(Ko Suk-Hwan) 한국태양에너지학회 2013 한국태양에너지학회 논문집 Vol.33 No.5

Wind resource data of short-term period has to be corrected a long-term period by using MCP method that Is a statistical method to predict the long-term wind resource at target site data with a reference site data. Because the field measurement for wind assessment is limited to a short period by various constraints. In this study, 2 different MCP methods such as Linear regression and Matrix method were chosen to compare the predictive accuracy between the methods. Finally long-term wind speed, wind power density and capacity factor at the target site for 20 years were estimated for the variability of wind and wind energy. As a result, for 20 years annual average wind speed, Yellow sea off shore wind farm was estimated to have 4.29% for coefficient of variation, CV, and-9.57%〜9.53% for range of variation, RV. It was predicted that the annual wind speed at Yellow sea offshore wind farm varied within±10%.

HeMOSU-1호 관측풍속의 불확실성을 고려한 서남해안의 풍력 발전량 예측

이기남(Lee, Geenam),김동현(Kim, Donghyawn),권오순(Kwon, Osoon) 한국신재생에너지학회 2014 신재생에너지 Vol.10 No.2

Wind power generation of 5 MW wind turbine was predicted by using wind measurement data from HeMOSU-1 which is at south west coast of Korea. Time histories of turbulent wind was generated from 10-min mean wind speed and then they were used as input to Bladed to estimated electric power. Those estimated powers are used in both polynominal regression and neural network training. They were compared with each other for daily production and yearly production. Effect of mean wind speed and turbulence intensity were quantitatively analyzed and discussed. This technique further can be used to assess lifetime power of wind turbine.

Application of Neural Network for Long-Term Correction of Wind Data

김현구(Kim, Hyun-Goo),Vaas, Franz 한국신재생에너지학회 2008 신재생에너지 Vol.4 No.4

Wind farm development project contains high business risks because that a wind farm, which is to be operating for 20 years, has to be designed and assessed only relying on a year or little more in-situ wind data. Accordingly, long-term correction of short-term measurement data is one of most important process in wind resource assessment for project feasibility investigation. This paper shows comparison of general Measure-Correlate-Prediction models and neural network, and presents new method using neural network for increasing prediction accuracy by accommodating multiple reference data. The proposed method would be interim step to complete long-term correction methodology for Korea, complicated Monsoon country where seasonal and diurnal variation of local meteorology is very wide.

풍력발전 예보시스템 KIER Forecaster의 개발

김현구(Kim, Hyun-Goo),장문석(Jang, Mun-Seok),경남호(Kyong, Nam-Ho),이영섭(Lee, Yung-Seop) 한국신재생에너지학회 2006 한국신재생에너지학회 학술대회논문집 Vol.2006 No.06

In the present paper a forecasting system of wind power generation for Walryong Site, Jejudo is presented, which has been developed and evaluated as a first step toward establishing Korea Forecasting Model of Wind Power Generation. The forecasting model, KIER forecaster is constructed based on statistical models and is trained with wind speed data observed at Gosan Weather Station nearby Walryong Si to. Due to short period of measurements at Walryong Site for training statistical model, Gosan wind data were substituted and transplanted to Walryong Site by using Measure-Correlate-Predict technique. Three-hour advanced forecast ins shows good agreement with the measurement at Walryong site with the correlation factor 0.88 and MAE(mean absolute error) 15% under.

풍력발전 예보시스템 KIER Forecaster의 개발

김현구(Kim, Hyun-Goo),이영섭(Lee, Yung-Seop),장문석(Jang, Mun-Seok),경남호(Kyong, Nam-Ho) 한국신재생에너지학회 2006 신재생에너지 Vol.2 No.2

In this paper, the first forecasting system of wind power generation, KIER Forecaster is presented. KIER Forecaster has been constructed based on statistical models and was trained with wind speed data observed at Gosan Weather Station nearby Walryong Site. Due to short period of measurements at Walryong Site for training the model, Gosan wind data were substituted and transplanted to Walryong Site by using Measure-Correlate-Predict(MCP) technique. The results of One to Three-hour advanced forecasting models are consistent with the measurement at Walryong site. In particular, the multiple regression model by classification of wind speed pattern, which has been developed in this work, shows the best performance comparing with neural network and auto-regressive models.

풍력자원평가를 위한 단순지형에서의 육상 기상탑 바람 데이터의 상호 적용

손진혁,고경남,허종철,김인행 한국동력기계공학회 2017 동력시스템공학회지 Vol.21 No.6

본 연구에서는 풍력자원평가 시에 단순지형에서 일정거리 이격된 기상탑의 바람데이터가 얼마만큼 상호 적용이 가능한지를 분석하였다. 분석 사이트는 제주도 지역의 김녕과 행원이며 두 지역은 4.31km 떨어져있고 단순지형이다. 분석에 적용된 데이터는 두 지역에서 취득한 1년간의 기상탑(Met-Mast) 70m 높이 풍속데이터와 구좌 지역에서 취득한 10년간의 자동 기상 관측 시스템(AWS) 10m 높이 풍속데이터이다. 신뢰성을 높이기 위하여 WindPRO 소프트웨어를 통해 두 지점의 바람데이터에 측정-상관-예측(MCP)법을 적용 후, 연간 에너지 생산량(AEP)과 이용률(CF)의 자기예측과 상호예측의 상대오차를 계산하였다. 그 결과, 상대오차는 1%미만이었고 본 연구와 같은 조건하에서 단순지형에서의 육상 기상탑 바람데이터가 상호 적용이 가능한 것으로 분석되었다.

풍력단지 운영자료와 재해석자료를 이용한 신안군 비금도 장기간 풍력자원평가

김현구,강용혁,윤창열,장문석 한국풍공학회 2013 한국풍공학회지 Vol.17 No.4

Long-term wind resource assessment has been carried out for Shinan-gun Bigeum-do where an offshore wind project is planning in succession of the onshore wind farm development. SCADA data of the Shinan Wind Power Plant and LIDAR measurements were selected as trustful wind resource datasets representing the local climatology by correlation analysis. Long-term correction has been carried out by the matrix time-series MCP taking MERRA reanalysis data as a reference dataset. The 34-year wind index was calculated from the long-term correction data sop that the variability of wind speed and wind power generation were evaluated as 2.0% and 5.4%, respectively, and its uncertainty were 1.2% and 2.8%, respectively. It is noticeable that wind index increased since 2010 comparing with long-term average for 34 years possibly due to an enforcement of La Niña effect. 해안 풍력단지 개발에 이어 해상 풍력단지 개발이 추진되고 있는 전라남도 신안군 비금도에 대한 장기간 풍력자원평가를 수행하였다. 해당 권역의 측정자료에 대한 상관분석을 통하여 신안풍력발전소 SCADA 자료와 LIDAR 측정자료를 유의한 풍력자원 측정자료로 선정하였으며, MERRA 재해석자료를 참조자료로 선택하여 Matrix Time Series MCP로 장기보정하였다. 장기보정 자료로부터 34년간의 바람인덱스를 산정하였는데 풍속과 풍력발전량의 변동률은 각각 2.0%, 5.4%이며 그 불확도는 각각 1.2%, 2.8%로 산정되었다. 특히 2010년부터 라니냐의 영향이 증대되며 바람인덱스가 평년 대비 높은 수준을 보이고 있음을 확인하였다.