Wind power curve modeling in complex terrain using statistical models

A power curve of a wind turbine describes the nonlinear relationship between wind speed and the corresponding power output. It shows the generation performance of a wind turbine. It plays vital roles in wind power forecasting, wind energy potential estimation, wind turbine selection, and wind turbine condition monitoring. In this paper, a hybrid power curve modeling technique is proposed. First, fuzzy c-means clustering is employed to detect and remove outliers from the original wind data. Then, different extreme learning machines are trained with the processed data. The corresponding wind power forecasts can also be obtained with the trained models. Finally, support vector regression is used to take advantage of different forecasts from different models. The results show that (1) five-parameter logistic function is superior to the others among the parametric models; (2) generally, nonparametric power curve models perform better than parametric models; (3) the proposed hybrid model can generate more accurate power output estimations than the other compared models, thus resulting in better wind turbine power curves. Overall, the proposed hybrid strategy can also be applied in power curve modeling, and is an effective tool to get better wind turbine power curves, even when the collected wind data is corrupted by outliers.

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Wind Turbine Power Curve Modelling with Logistic Functions Based on Quantile Regression

Applied Sciences ◽

10.3390/app11073048 ◽

2021 ◽

Vol 11 (7) ◽

pp. 3048

Author(s):

Bo Jing ◽

Zheng Qian ◽

Hamidreza Zareipour ◽

Yan Pei ◽

Anqi Wang

Keyword(s):

Quantile Regression ◽

Wind Turbine ◽

Wind Power ◽

Power Distribution ◽

Wind Farms ◽

Power Curve ◽

Energy Assessment ◽

Curve Modeling ◽

Relationship Of ◽

Logistic Functions

The wind turbine power curve (WTPC) is of great significance for wind power forecasting, condition monitoring, and energy assessment. This paper proposes a novel WTPC modelling method with logistic functions based on quantile regression (QRLF). Firstly, we combine the asymmetric absolute value function from the quantile regression (QR) cost function with logistic functions (LF), so that the proposed method can describe the uncertainty of wind power by the fitting curves of different quantiles without considering the prior distribution of wind power. Among them, three optimization algorithms are selected to make comparative studies. Secondly, an adaptive outlier filtering method is developed based on QRLF, which can eliminate the outliers by the symmetrical relationship of power distribution. Lastly, supervisory control and data acquisition (SCADA) data collected from wind turbines in three wind farms are used to evaluate the performance of the proposed method. Five evaluation metrics are applied for the comparative analysis. Compared with typical WTPC models, QRLF has better fitting performance in both deterministic and probabilistic power curve modeling.

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

Abstract and Applied Analysis ◽

10.1155/2014/941648 ◽

2014 ◽

Vol 2014 ◽

pp. 1-15 ◽

Cited By ~ 3

Author(s):

Zhenhai Guo ◽

Xia Xiao

Keyword(s):

Wind Speed ◽

Wind Power ◽

Distribution Functions ◽

Good Effect ◽

Detailed Knowledge ◽

Power Curve ◽

Accurate Assessment ◽

Local Wind ◽

Modeling Methods ◽

Curve Modeling

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.

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