摘要：AbstractA normal behavior model based on a boosted stacked regressor, trained in a k-fold crossvalidation with optimal power curve data, is proposed for wind turbine fault detection. In order to obtain the optimal power curve data, a signal processing scheme based on density-based spatial clustering of applications with noise, along with a robust estimation algorithm are employed, from which an upper-lower bound envelop is established. The experimental results based on real wind turbine data from supervisory control and data acquisition system indicate the effectiveness and impact of the proposed method in practical applications
关键词：KeywordsPower curvewind turbinefault detectionSCADAnormal behavior model
