Abstract and Applied Analysis

Wind Power Assessment Based on a WRF Wind Simulation with Developed Power Curve Modeling Methods

Zhenhai Guo and Xia Xiao

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Abstract

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.

Article information

Source
Abstr. Appl. Anal., Volume 2014, Special Issue (2014), Article ID 941648, 15 pages.

Dates
First available in Project Euclid: 6 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.aaa/1412606353

Digital Object Identifier
doi:10.1155/2014/941648

Zentralblatt MATH identifier
07023360

Citation

Guo, Zhenhai; Xiao, Xia. Wind Power Assessment Based on a WRF Wind Simulation with Developed Power Curve Modeling Methods. Abstr. Appl. Anal. 2014, Special Issue (2014), Article ID 941648, 15 pages. doi:10.1155/2014/941648. https://projecteuclid.org/euclid.aaa/1412606353


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