Spatial-temporal Pattern Recognition for Data Identification and Tagging Based on Power Curve in Wind Turbines

Abstract

Due to variational environmental conditions and varied adaptive control strategies, the operation states of wind turbines are continuously changing, leading to diverse types of samples in the power curve. Different kinds of samples may contain noises or valuable information for specific downstream tasks and thus need to be correctly identified and labeled. To this end, this paper proposes a spatial-temporal pattern recognition algorithm for data identification and tagging. According to spatial distribution and temporal characteristics, all data points are divided into four groups including normal samples, isolated outliers, change points, and faulty samples. Then, some distances based on the dynamic time warping method are defined to make evaluations and then serve as indicators for achieving precise tagging of each category. Case studies and comparative experiments are conducted to verify the effectiveness and superiority of the proposed method.