Abnormal Data Cleaning Method for Wind Turbines Based on Constrained Curve Fitting

Xiangqing Yin, Yi Liu, Li Yang and Wenchao Gao ()
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Xiangqing Yin: School of Mechanical Electronic and Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Yi Liu: School of Mechanical Electronic and Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Li Yang: School of Mechanical Electronic and Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Wenchao Gao: School of Mechanical Electronic and Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China

Energies, 2022, vol. 15, issue 17, 1-22

Abstract: With the increase of the scale of wind turbines, the problem of data quality of wind turbines has become increasingly prominent, which seriously affects the follow-up research. A large number of abnormal data exist in the historical data recorded by the wind turbine Supervisory Control And Data Acquisition (SCADA) system. In order to improve data quality, it is necessary to clean a large number of abnormal data in the original data. Aiming at the problem that the cleaning effect is not good in the presence of a large number of abnormal data, a method for cleaning abnormal data of wind turbines based on constrained curve fitting is proposed. According to the wind speed-power characteristics of wind turbines, the constrained wind speed-power curve is fit with the least square method, and the constrained optimization problem is transformed into an unconstrained optimization problem by using the external penalty function method. Data cleaning was performed on the fitted curve using an improved 3-σ standard deviation. Experiments show that, compared with the existing methods, this method can still perform data cleaning well when the historical wind turbine data contains many abnormal data, and the method is insensitive to parameters, simple in the calculation, and easy to automate.

Keywords: data cleaning; wind turbine; wind power; constrained curve fitting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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