CAJ | 학술논문

在对风电场风能资源进行评估时，主要依据测风数据及气象站数据，但由于测风数据往往缺测或不合理，而距离最近的气象站所提供的气象数据与同期的测风数据的相关性一般较差。鉴于此，引入中尺度数值天气预测模型COAMPS对湖南T风电场工程区域内风资源进行评估分析，并首次将中尺度数据应用于测风塔缺测和不合理数据的插补中。结果表明，中尺度插补订正后的数据与实测数据的月平均值误差为4.87%，标准偏差误差为9.8%，该模型能够较准确地反映风电场风能资源的实际情况。
재대풍전장풍능자원진행평고시，주요의거측풍수거급기상참수거，단유우측풍수거왕왕결측혹불합리，이거리최근적기상참소제공적기상수거여동기적측풍수거적상관성일반교차。감우차，인입중척도수치천기예측모형COAMPS대호남T풍전장공정구역내풍자원진행평고분석，병수차장중척도수거응용우측풍탑결측화불합리수거적삽보중。결과표명，중척도삽보정정후적수거여실측수거적월평균치오차위4.87%，표준편차오차위9.8%，해모형능구교준학지반영풍전장풍능자원적실제정황。
The wind power resources assessment of a wind power station is mainly based on the measured data from the wind tower and the weather station, but the data from the wind tower is often incomplete or unreasonable and the data from the nearest weather station is poorly related to the measured data of wind tower in the same time. For this reason, a mesoscale numerical weather forecast model COAMPS is introduced to analyze and assess the wind power resources of a wind power station in Hunan Province. The mesoscale data is firstly used to interpolate the incomplete or unreasonable data of wind tower. The results show that the error of the monthly average data revised by interpolation and the measured data is 4. 87%, the standard error is 9. 8%. So the mesoscale COPMAS can accurately reflect the practical wind resources of wind power plants.