WRF气象模式在高海拔复杂山地风电场中的应用研究
|
摘要
为深入研究高海拔无测风复杂地形区域风能资源评估,应用WRF气象模式进行风能资源分布模拟,并将模拟结果、MERRA数据分别与实测数据进行对比分析。研究表明,WRF模拟具有较高的模拟精度,且效果明显优于MERRA数据。WRF模式模拟值与6座测风塔实测年、月、日平均风速绝对误差均值为0. 37、0. 61 m/s和1. 36 m/s,日平均风速的相关系数均值可达0. 709 5。
In order to deeply study the wind resource assessment in high-altitude mountainous wind farm without anemometer tower,the weather research and forecasting( WRF) model is used to simulate the distribution of wind energy resources,and the simulation results and MERRA data are compared with actual measured data. The comparison results show that the WRF simulation has higher accuracy and the effect is obviously better than MERRA data. The average error between WRF simulation value and measured average annual,monthly and daily wind speed of six anemometer towers is 0. 37 m/s,0. 61 m/s and 1. 36m/s respectively,and the correlation coefficient of daily average wind speed can reach 0. 709 5.
In order to deeply study the wind resource assessment in high-altitude mountainous wind farm without anemometer tower,the weather research and forecasting( WRF) model is used to simulate the distribution of wind energy resources,and the simulation results and MERRA data are compared with actual measured data. The comparison results show that the WRF simulation has higher accuracy and the effect is obviously better than MERRA data. The average error between WRF simulation value and measured average annual,monthly and daily wind speed of six anemometer towers is 0. 37 m/s,0. 61 m/s and 1. 36m/s respectively,and the correlation coefficient of daily average wind speed can reach 0. 709 5.
引文
[1]郁永静,何一,刘志远.基于最大熵原理的风电场风频分布研究[J].水力发电,2017,43(6):93-96.
[2]牟磊,刘天龙.利用STRM和NCEP数据提高风电场宏观选址的准确性[J].电网与清洁能源,2009,25(8):54-58.
[3]穆海振,徐家良,杨永辉.数值模拟在上海海上风能资源评估中的应用[J].高原气象,2009,27(B12):196-202.
[4] HRAYSHAT E S. Wind resource assessment of the Jordanian southern region[J]. Renewable Energy,2007,32(11):1948-1960.
[5] HENNESSEY J P Jr. Some aspects of wind power statistics[J]. Journal of Applied Meteorology,1977,16(2):119-128.
[6]张华,郁永静,冯志军,等.基于小波分解与支持向量机的风速预测模型[J].水力发电学报,2012,31(1):208-212.
[7]刘志远,李良县,任腊春.插补测风塔缺测数据的相关性计算方法讨论[J].可再生能源,2016,34(9):1342-1347.
[2]牟磊,刘天龙.利用STRM和NCEP数据提高风电场宏观选址的准确性[J].电网与清洁能源,2009,25(8):54-58.
[3]穆海振,徐家良,杨永辉.数值模拟在上海海上风能资源评估中的应用[J].高原气象,2009,27(B12):196-202.
[4] HRAYSHAT E S. Wind resource assessment of the Jordanian southern region[J]. Renewable Energy,2007,32(11):1948-1960.
[5] HENNESSEY J P Jr. Some aspects of wind power statistics[J]. Journal of Applied Meteorology,1977,16(2):119-128.
[6]张华,郁永静,冯志军,等.基于小波分解与支持向量机的风速预测模型[J].水力发电学报,2012,31(1):208-212.
[7]刘志远,李良县,任腊春.插补测风塔缺测数据的相关性计算方法讨论[J].可再生能源,2016,34(9):1342-1347.