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.
Highlights
This section employs the single layer feed-forward neural network (SLFN) to model the complex relationship between wind speed and actual power output, which is usually mismatched according to the wind turbines (WTs) power curve provided by the manufacturer
To build up wind farms, it is essential to perform an accurate assessment of the wind energy potential at the promising
The statistical analysis, concerning the use of the various pdfs to describe the wind speed frequency distributions, is generally used. Their performance is expected to be enhanced in the actual applications. This original method of wind power assessment is a combination of the weather research and forecasting (WRF) wind speed simulation and the SLFN algorithm, due to the recognized strengths of both methods
Summary
The development of new wind projects persists in being hampered by the lack of reliable and accurate wind resource data in many regions of the world Such data are needed to enable governments, private developers, and others to determine the priority that should be given to wind energy utilization and to identify the potential areas that might be suitable for development [2, 3]. Considering the stage-construction project of a wind farm, power assessment is crucial to determine the future investment behavior for both the development scale and the operation mode of a wind farm In this case, future power estimations cannot be directly obtained from the historical power records, mainly due to the change of installation. The feasible method is to model the transition relationship from wind speed to turbine power output
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