A Comprehensive Cleaning Method for Outliers in Wind Turbine Power Curves Based on the Quartile Method and Segmented Regression Detection Method

The actual power curve of a wind turbine is essential for performance evaluation and operational optimization. However, SCADA data frequently contain various abnormal data points that limit their direct and effective use. Existing methods often fail to provide high-quality data for accurate power-curve fitting. Therefore, this paper proposes a comprehensive outlier cleaning method (QRD). This method incorporates the operational mechanisms of wind turbines and establishes preprocessing rules to effectively remove extreme outliers and bottom horizontal accumulation exhibiting distinct numerical characteristics. By leveraging the data distribution features in pitch angle–power and wind speed–power relationships, it implements horizontal and vertical quartile methods to eliminate mid-level accumulation and discrete outliers. A segmented regression-based outlier detection method with metrics adaptive to the power-curve distribution characteristics is proposed to clean residual outliers. Comparative results demonstrate that, relative to the Bins, CPQ, CIF, and TTLOF methods, the QRD method achieves a cleaning speed of 0.152 s per 10,000 data points, improving the average dispersion difference by 32.94%, 11.74%, 13.05%, and 9.67%, respectively. In terms of power-curve fitting accuracy, the average NMAE decreases by 8.65%, 5.07%, 7.57%, and 4.06%, while the average NRMSE decreases by 10.78%, 7.99%, 7.66%, and 5.16% and R2 increases by 1.74%, 1.62%, 1.57%, and 1.03%, respectively. Overall, QRD demonstrates superior efficiency and accuracy in identifying abnormal wind power values, providing reliable support for high-quality power-curve modeling.