Wind Power Assessment Based on a WRF Wind Simulation with Developed Power Curve Modeling Methods
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 g...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2014/941648 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832548693218164736 |
|---|---|
| author | Zhenhai Guo Xia Xiao |
| author_facet | Zhenhai Guo Xia Xiao |
| author_sort | Zhenhai Guo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a33f61d4448949a09e3215457d63d70b |
| institution | Kabale University |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-a33f61d4448949a09e3215457d63d70b2025-02-03T06:13:20ZengWileyAbstract and Applied Analysis1085-33751687-04092014-01-01201410.1155/2014/941648941648Wind Power Assessment Based on a WRF Wind Simulation with Developed Power Curve Modeling MethodsZhenhai Guo0Xia Xiao1State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, ChinaState Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, ChinaThe 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.http://dx.doi.org/10.1155/2014/941648 |
| spellingShingle | Zhenhai Guo Xia Xiao Wind Power Assessment Based on a WRF Wind Simulation with Developed Power Curve Modeling Methods Abstract and Applied Analysis |
| title | Wind Power Assessment Based on a WRF Wind Simulation with Developed Power Curve Modeling Methods |
| title_full | Wind Power Assessment Based on a WRF Wind Simulation with Developed Power Curve Modeling Methods |
| title_fullStr | Wind Power Assessment Based on a WRF Wind Simulation with Developed Power Curve Modeling Methods |
| title_full_unstemmed | Wind Power Assessment Based on a WRF Wind Simulation with Developed Power Curve Modeling Methods |
| title_short | Wind Power Assessment Based on a WRF Wind Simulation with Developed Power Curve Modeling Methods |
| title_sort | wind power assessment based on a wrf wind simulation with developed power curve modeling methods |
| url | http://dx.doi.org/10.1155/2014/941648 |
| work_keys_str_mv | AT zhenhaiguo windpowerassessmentbasedonawrfwindsimulationwithdevelopedpowercurvemodelingmethods AT xiaxiao windpowerassessmentbasedonawrfwindsimulationwithdevelopedpowercurvemodelingmethods |