Abstract.A data-mining approach is proposed to investigate the power generation monitoring of wind turbine based on power curve profiles in this paper. The weakened power generation performance could be identified by this method through assessing the wind-speed power datasets. Shapes of wind power curve profiles over consecutive time intervals are constructed by fitting power curve models into wind-speed power datasets. In this research, a optimal constraint in each sub-dataset is developed for governing the data-driven windpower generation method based on distance-based outlier detection and variance analysis model. The Auto-adapt Optimal Interclass Variance algorithm realize the self-optimization of the threshold parameter and achieves a high degree of robustness to the variations in wind-power generation performance monitoring. The blind industrial researches are conducted to validate the effectiveness of the approach, and shows the decrease of error rates when detecting weakened power generation performance or causing financial loss.

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