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

1
School of Instrumentation and Optoelectronic Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, China
2
Department of Electrical and Computer Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada
3
State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute, No. 15 Xiaoying East Road, Qinghe, Beijing 100192, China
*
Author to whom correspondence should be addressed.
Academic Editor: Mohsen N. Soltani
Appl. Sci. 2021, 11(7), 3048; https://doi.org/10.3390/app11073048
Received: 22 February 2021 / Revised: 24 March 2021 / Accepted: 25 March 2021 / Published: 29 March 2021
(This article belongs to the Section Energy)
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. View Full-Text
Keywords: logistic function; quantile regression; outlier filtering; wind turbine power curve; wind power logistic function; quantile regression; outlier filtering; wind turbine power curve; wind power
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MDPI and ACS Style

Jing, B.; Qian, Z.; Zareipour, H.; Pei, Y.; Wang, A. Wind Turbine Power Curve Modelling with Logistic Functions Based on Quantile Regression. Appl. Sci. 2021, 11, 3048. https://doi.org/10.3390/app11073048

AMA Style

Jing B, Qian Z, Zareipour H, Pei Y, Wang A. Wind Turbine Power Curve Modelling with Logistic Functions Based on Quantile Regression. Applied Sciences. 2021; 11(7):3048. https://doi.org/10.3390/app11073048

Chicago/Turabian Style

Jing, Bo, Zheng Qian, Hamidreza Zareipour, Yan Pei, and Anqi Wang. 2021. "Wind Turbine Power Curve Modelling with Logistic Functions Based on Quantile Regression" Applied Sciences 11, no. 7: 3048. https://doi.org/10.3390/app11073048

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