Abstract
In this paper, a data driven method for Wind Turbine system level anomaly detection and root sub-component identification is proposed. Supervisory control and data acquisition system (SCADA) data of WT is adopted and several parameters are selected based on physical knowledge in this domain and correlation coefficient analysis to build a normal behavior model. This model which is based on Self-organizing map (SOM) projects higher-dimensional SCADA data into a two-dimension-map. Afterwards, the Euclidean distance based indicator for system level anomalies is defined and a filter is created to screen out suspicious data points based on quantile function. Moreover, a failure data pattern based criterion is created for anomaly detection from system level. In order to track which sub-component should be responsible for an anomaly, a contribution proportion (CP) index is proposed. The method is tested with a two-month SCADA dataset with the measurement interval as 20 seconds. Results demonstrate capability and efficiency of the proposed method.
Highlights
Wind energy is considered an effective way to relieve the carbon dioxide risk caused by consuming traditional fossil resources
In (Wilkinson, Darnell, Delft, & Harman, 2014), a comparison study is conducted among neural network (NN), Self-organizing map (SOM) and physical model based wind turbine condition monitoring with SCADA data
Wind turbine SCADA data is adopted and several parameters are selected based on physic knowledge and correlation coefficient analysis for normal behavior modeling
Summary
Wind energy is considered an effective way to relieve the carbon dioxide risk caused by consuming traditional fossil resources. Considering SOM based wind turbine anomaly detection, many works have been published. In (Wilkinson, Darnell, Delft, & Harman, 2014), a comparison study is conducted among NN, SOM and physical model based wind turbine condition monitoring with SCADA data. It fails to define the abnormal conditions after the deviation is calculated. The second difference is that the proposed method only uses SCADA as data source This brings challenges for quantifying anomalies based on deviation signal.
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 callMore From: International Journal of Prognostics and Health Management
Disclaimer: 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.
