风电场风速概率Weibull分布的参数估计研究
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摘要
风电场风速概率分布是描述风能特征的主要指标,其准确程度直接影响风电场风能资源的评估结果。主要介绍了两参数威布尔分布的极大似然估计法、最小二乘估计法和WASP估计法3种风速概率分布参数的估计方法。通过对四川广元地区低风速区域测风塔实测数据分析,结果表明,极大似然估计法与实测数据统计结果最为接近,拟合效果良好;Weibull参数c、k存在相对较为明显的季节变化;尺度参数c值随高度呈现幂指数形式,形状参数k值随高度呈现二次函数形式变化特征,在80～90 m高度左右,曲线出现拐点,k值取得最大值。
The probability distribution of wind speed is the main index to describe the characteristics of wind energy, and its accuracy directly affects the evaluation results of wind energy resources in wind farmers. In this paper, the maximum likelihood estimation method, least squares estimation method and the WASP method for estimating the wind speed probability distribution parameters of two-parameter Weibull distribution are introduced. Through the analysis of the measured data of the wind tower in the low wind speed area in Guangyuan area of Sichuan, the results show that the maximum likelihood estimation method and the measured data are the closest, and the fitting effect is good; the Weibull parameters c and k have relatively obvious seasonal changes; The shape parameter value exhibits a power exponential form with height, and the k value exhibits a quadratic function form variation characteristic with height which has an inflection point and gets the maximum value around the 80～90 meters.
The probability distribution of wind speed is the main index to describe the characteristics of wind energy, and its accuracy directly affects the evaluation results of wind energy resources in wind farmers. In this paper, the maximum likelihood estimation method, least squares estimation method and the WASP method for estimating the wind speed probability distribution parameters of two-parameter Weibull distribution are introduced. Through the analysis of the measured data of the wind tower in the low wind speed area in Guangyuan area of Sichuan, the results show that the maximum likelihood estimation method and the measured data are the closest, and the fitting effect is good; the Weibull parameters c and k have relatively obvious seasonal changes; The shape parameter value exhibits a power exponential form with height, and the k value exhibits a quadratic function form variation characteristic with height which has an inflection point and gets the maximum value around the 80～90 meters.
引文
[1] 李柯,何凡能,席建超.中国陆地风能资源开发潜力区域分析[J].资源科学,2008,32(9):1672-1678.
[2] 朱瑞兆,薛桁.风能的计算和我国风能的分布[J].气象,1981,7(8):26-28.
[3] 王淼,曾利华.风速频率分布模型的研究[J].水力发电学报,2011,30(6):204-209.
[4] 李慧,孙宏斌,张芳.风电场风速分布模型研究综述[J].电工电能新技术,2014,33(8):62-66.
[5] Garcia A, Torres J L, Prieto E, et al. Fitting wind speed distributions: a case study[J]. Solar Energy,1998, 62(2):139-144.
[6] Dorvlo A S S, Estimating wind speed distribution[J].Energy Conversion & Management, 2002, 43(17): 2311- 2318.
[7] Lun I Y F, Lam J C. A study of Weibull parameters using long-term wind observations [J]. Renewable Energy, 2000, 20(2):145-153.
[8] 李军,胡非,王丙兰,等.风速的Weibull分布参数[J].太阳能学报, 2012,33(10):1667-1671.
[9] 龚伟俊,李为相,张广明.基于威布尔分布的风速概率分布参数估计方法[J].可再生能源,2011,29(6):20-23.
[10] 陈练,李栋梁,吴洪宝.中国风速概率分布及在风能评估中的应用[J].太阳能学报,2010,31(9):1209-1214.
[11] 严彦,许昌,刘德有,等.测风数据的时间间隔对风速概率分布参数计算的影响[J].可再生能源,2011, 29(6):24-28.
[12] 丁明,吴义纯,张立军.风电场风速概率分布参数计算方法的研究[J].中国电机工程学报,2005, 25(10): 107-110.
[13] Baseer M A, Meyer J P, Rehman S, et al. Wind power characteristics of seven data collection sites in Jubail, Saudi Arabia using Weibull parameters[J]. Renewable Energy, 2017,102:35-49.
[14] 包能胜,刘军峰,倪维斗,等.新疆达坂城风电场风能资源特性分析[J].太阳能学报,2006, 27(11):1073-1077.
[15] 杨振斌,朱瑞兆,薛桁.风电场风能资源评价两个新参数——相当风速、有功风功率密度[J].太阳能学报,2007,28(3):248-251.
[16] 姜广绪,潘晶雯,田景奎.双参数威布尔分布风况中基于k值分析的能量分布研究[J].电力建设, 2015,36(3): 105-108.
[2] 朱瑞兆,薛桁.风能的计算和我国风能的分布[J].气象,1981,7(8):26-28.
[3] 王淼,曾利华.风速频率分布模型的研究[J].水力发电学报,2011,30(6):204-209.
[4] 李慧,孙宏斌,张芳.风电场风速分布模型研究综述[J].电工电能新技术,2014,33(8):62-66.
[5] Garcia A, Torres J L, Prieto E, et al. Fitting wind speed distributions: a case study[J]. Solar Energy,1998, 62(2):139-144.
[6] Dorvlo A S S, Estimating wind speed distribution[J].Energy Conversion & Management, 2002, 43(17): 2311- 2318.
[7] Lun I Y F, Lam J C. A study of Weibull parameters using long-term wind observations [J]. Renewable Energy, 2000, 20(2):145-153.
[8] 李军,胡非,王丙兰,等.风速的Weibull分布参数[J].太阳能学报, 2012,33(10):1667-1671.
[9] 龚伟俊,李为相,张广明.基于威布尔分布的风速概率分布参数估计方法[J].可再生能源,2011,29(6):20-23.
[10] 陈练,李栋梁,吴洪宝.中国风速概率分布及在风能评估中的应用[J].太阳能学报,2010,31(9):1209-1214.
[11] 严彦,许昌,刘德有,等.测风数据的时间间隔对风速概率分布参数计算的影响[J].可再生能源,2011, 29(6):24-28.
[12] 丁明,吴义纯,张立军.风电场风速概率分布参数计算方法的研究[J].中国电机工程学报,2005, 25(10): 107-110.
[13] Baseer M A, Meyer J P, Rehman S, et al. Wind power characteristics of seven data collection sites in Jubail, Saudi Arabia using Weibull parameters[J]. Renewable Energy, 2017,102:35-49.
[14] 包能胜,刘军峰,倪维斗,等.新疆达坂城风电场风能资源特性分析[J].太阳能学报,2006, 27(11):1073-1077.
[15] 杨振斌,朱瑞兆,薛桁.风电场风能资源评价两个新参数——相当风速、有功风功率密度[J].太阳能学报,2007,28(3):248-251.
[16] 姜广绪,潘晶雯,田景奎.双参数威布尔分布风况中基于k值分析的能量分布研究[J].电力建设, 2015,36(3): 105-108.