Pre-Filtering SCADA Data for Enhanced Machine Learning-Based Multivariate Power Estimation in Wind Turbines

Abstract

Data generated during the shutdown or start-up processes of wind turbines, particularly in complex wind conditions such as offshore environments, often accumulate in the low-wind-speed region, leading to reduced multivariate power estimation accuracy. Therefore, developing efficient filtering methods is crucial to improving data quality and model performance. This paper proposes a novel filtering method that integrates the control strategies of variable-speed, variable-pitch wind turbines, such as maximum-power point tracking (MPPT) and pitch angle control, with statistical distribution characteristics derived from supervisory control and data acquisition (SCADA). First, thresholds for pitch angle and rotor speed are determined based on SCADA data distribution, and the filtering effect is visualized. Subsequently, a sliding window technique is employed for the secondary confirmation of potential outliers, enabling further anomaly detection (AD). Finally, the performance of the power estimation model is validated using two wind turbine datasets and two machine learning algorithms, with results compared with and without filtering. The results demonstrate that the proposed filtering method significantly enhances the accuracy of multivariate power estimation, proving its effectiveness in improving data quality for wind turbines operating in diverse and complex environments.