A SOM based Anomaly Detection Method for Wind Turbines Health Management through SCADA Data

Mian Du, Shicong Ma, Qing He, Lin Cheng, Jianbo Guo, and Lina Bertling Tjernberg
Publication Target: 
Publication Issue: 
Special Issue Big Data and Analytics
Submission Type: 
Full Paper
ijphm_16_029.pdf1.83 MBOctober 31, 2016 - 5:12pm

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 physic knowledge 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 data-set with the measurement interval as 20 seconds. Results demonstrate capability and efficiency of the proposed method.

Publication Year: 
Publication Volume: 
Publication Control Number: 
Page Count: 
Submission Keywords: 
anomaly detection
Self-organizing maps
Wind Turbine
Critical Component
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Submitted by: 

follow us

PHM Society on Facebook Follow PHM Society on Twitter PHM Society on LinkedIn PHM Society RSS News Feed