二参数Weibull分布在风能资源参数长年代订正中的应用探讨
发布时间：2019-05-10 05:42
【摘要】：选择代表平原、丘陵和山区的6个气象站分别作为参证站和订正站,采用二参数Weibull分布法和线性回归法进行30 a订正效果检验,结果表明:短期资料进行风电场风能资源评估,误差较大;二参数Weibull分布法适宜不同地形风能资源参数长年代平均订正,年平均风功率密度的订正效果更好;线性回归法适宜于订正站和参证站线性相关性较好的长年代平均订正;使用2 a以上的观测资料,订正效果较1 a资料有所提高。
[Abstract]:Six meteorological stations representing plain, hilly and mountainous areas were selected as reference stations and revised stations, respectively. Two-parameter Weibull distribution method and linear regression method were used to test the effect of 30-year revision. The results showed that short-term data were used to evaluate wind energy resources of wind farms. The error is large; The two-parameter Weibull distribution method is suitable for the long-term mean correction of wind energy resource parameters in different terrain, and the annual average wind power density correction effect is better, and the linear regression method is suitable for the long-term average correction with good linear correlation between the revised station and the reference station. Using the observation data of more than 2 years, the effect of the revision is higher than that of the data of 1 year.
【作者单位】： 吉林省气候中心;
【基金】：中国气象局气候变化专项(CCSF201232)
【分类号】：TM614
[Abstract]:Six meteorological stations representing plain, hilly and mountainous areas were selected as reference stations and revised stations, respectively. Two-parameter Weibull distribution method and linear regression method were used to test the effect of 30-year revision. The results showed that short-term data were used to evaluate wind energy resources of wind farms. The error is large; The two-parameter Weibull distribution method is suitable for the long-term mean correction of wind energy resource parameters in different terrain, and the annual average wind power density correction effect is better, and the linear regression method is suitable for the long-term average correction with good linear correlation between the revised station and the reference station. Using the observation data of more than 2 years, the effect of the revision is higher than that of the data of 1 year.
【作者单位】： 吉林省气候中心;
【基金】：中国气象局气候变化专项(CCSF201232)
【分类号】：TM614
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