Wind turbine anomaly detection based on SCADA data mining

Abstract

In this paper, a wind turbine anomaly detection method based on a generalized feature extraction is proposed. Firstly, wind turbine (WT) attributes collected from the Supervisory Control And Data Acquisition (SCADA) system are clustered with k-means, and the Silhouette Coefficient (SC) is adopted to judge the effectiveness of clustering. Correlation between attributes within a class becomes larger, correlation between classes becomes smaller by clustering. Then, dimensions of attributes within classes are reduced based on t-Distributed-Stochastic Neighbor Embedding (t-SNE) so that the low-dimensional attributes can be more full and more concise in reflecting the WT attributes. Finally, the detection model is trained and the normal or abnormal state is detected by the classification result 0 or 1 respectively. Experiments consists of three cases with SCADA data demonstrate the effectiveness of the proposed method.