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.
Highlights
With the increasing exhaustion of resources such as minerals and petroleum, wind energy is widely used due to its sustainability and cleanliness
Due to the above problems, this paper propose the following method: first, we cluster the attributes collected from Supervisory Control And Data Acquisition (SCADA), and reduce the dimensions
wind turbine (WT) attributes collected from the SCADA system are clustered by k-means, and the method of dimension reduction within class based on t-Distributed-Stochastic Neighbor Embedding (t-SNE) is proposed
Summary
With the increasing exhaustion of resources such as minerals and petroleum, wind energy is widely used due to its sustainability and cleanliness. In [4], an evaluation index of wind turbine generator operating health based on the relationships with SCADA data was presented. In [6], a wind turbine generator slip ring damage detection through temperature data analysis method was presented. The rich data of SCADA system make anomaly detection of wind turbines more flexible and reliable. In [11], based on fuzzy theory, a generalized wind turbine anomaly detection model is proposed. In [12], a SVM-based method for fault detection in wind turbines was proposed, and the operating states of the wind turbine is classified.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
