Wind Turbine Power Curve Modelling with Logistic Functions Based on Quantile Regression
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 funct...
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| Format: | Article |
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MDPI AG
2021-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/7/3048 |
| _version_ | 1827696434535727104 |
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| author | Bo Jing Zheng Qian Hamidreza Zareipour Yan Pei Anqi Wang |
| author_facet | Bo Jing Zheng Qian Hamidreza Zareipour Yan Pei Anqi Wang |
| author_sort | Bo Jing |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T12:48:32Z |
| format | Article |
| id | doaj.art-904e4ab993e84e409a5b3d064644d3ef |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T12:48:32Z |
| publishDate | 2021-03-01 |
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| series | Applied Sciences |
| spelling | doaj.art-904e4ab993e84e409a5b3d064644d3ef2023-11-21T13:18:48ZengMDPI AGApplied Sciences2076-34172021-03-01117304810.3390/app11073048Wind Turbine Power Curve Modelling with Logistic Functions Based on Quantile RegressionBo Jing0Zheng Qian1Hamidreza Zareipour2Yan Pei3Anqi Wang4School of Instrumentation and Optoelectronic Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, ChinaSchool of Instrumentation and Optoelectronic Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, ChinaDepartment of Electrical and Computer Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, CanadaState Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute, No. 15 Xiaoying East Road, Qinghe, Beijing 100192, ChinaSchool of Instrumentation and Optoelectronic Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, ChinaThe 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.https://www.mdpi.com/2076-3417/11/7/3048logistic functionquantile regressionoutlier filteringwind turbine power curvewind power |
| spellingShingle | Bo Jing Zheng Qian Hamidreza Zareipour Yan Pei Anqi Wang Wind Turbine Power Curve Modelling with Logistic Functions Based on Quantile Regression Applied Sciences logistic function quantile regression outlier filtering wind turbine power curve wind power |
| title | Wind Turbine Power Curve Modelling with Logistic Functions Based on Quantile Regression |
| title_full | Wind Turbine Power Curve Modelling with Logistic Functions Based on Quantile Regression |
| title_fullStr | Wind Turbine Power Curve Modelling with Logistic Functions Based on Quantile Regression |
| title_full_unstemmed | Wind Turbine Power Curve Modelling with Logistic Functions Based on Quantile Regression |
| title_short | Wind Turbine Power Curve Modelling with Logistic Functions Based on Quantile Regression |
| title_sort | wind turbine power curve modelling with logistic functions based on quantile regression |
| topic | logistic function quantile regression outlier filtering wind turbine power curve wind power |
| url | https://www.mdpi.com/2076-3417/11/7/3048 |
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