Wind Power Assessment Based on a WRF Wind Simulation with Developed Power Curve Modeling Methods
Wind Power Assessment Based on a WRF Wind Simulation with Developed Power Curve Modeling Methods
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DOI：
10.1155/2014/941648
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发表时间：
2014-07
影响因子：
--
通讯作者：
Zhen-hai Guo;X. Xiao
中科院分区：
文献类型：
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作者：
Zhen-hai Guo;X. Xiao
准确评估风电场的发电潜力不仅需要对当地的风力资源有详细的了解，还需要有一个效果良好的风电场等效功率曲线。虽然风速的概率分布函数（pdf）是常用的，但它们表面上良好的分布性能并不总是转化为准确的发电评估。本文基于气象研究与预报（WRF）的风速模拟和两种改进的功率曲线建模方法，对风力发电功率评估的发展做出了贡献。这些方法是对本文采用单层前馈神经网络（SLFN）拟合功率曲线建模方法的改进;此外，还采用了数据质量检查和异常点检测技术以及方向曲线建模方法，有效地提高了原模型的性能。WRF-SLFN-OD和WRF-SLFN-WD两种方法能够避免功率曲线建模过程中异常输出的干扰和局部风速的方向性影响。通过对中国北方3个测站的数据进行仿真，结果表明，与原有方法相比，本文提出的两种方法具有较强的能力，能够更准确地评估风电场的发电潜力。
The accurate assessment of wind power potential requires not only the detailed knowledge of the local wind resource but also an equivalent power curve with good effect for a local wind farm. Although the probability distribution functions (pdfs) of the wind speed are commonly used, their seemingly good performance for distribution may not always translate into an accurate assessment of power generation. This paper contributes to the development of wind power assessment based on the wind speed simulation of weather research and forecasting (WRF) and two improved power curve modeling methods. These approaches are improvements on the power curve modeling that is originally fitted by the single layer feed-forward neural network (SLFN) in this paper; in addition, a data quality check and outlier detection technique and the directional curve modeling method are adopted to effectively enhance the original model performance. The proposed two methods, named WRF-SLFN-OD and WRF-SLFN-WD, are able to avoid the interference from abnormal output and the directional effect of local wind speed during the power curve modeling process. The data examined are from three stations in northern China; the simulation indicates that the two developed methods have strong abilities to provide a more accurate assessment of the wind power potential compared with the original methods.