【Title】A SCADA Data based Anomaly Detection Method for Wind Turbines
【Abstract】in this paper, a data driven method for Wind Turbine system level anomaly detection is proposed. Supervisory control and data acquisition system (SCADA) data of a wind turbine is adopted and several parameters are selected based on physic knowledge and correlation coefficient analysis to build a normal behavior model. This model is based on Self-organizing map (SOM) which can project higher dimensional SCADA data into a two-dimension-map. After that, 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. 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.
【Keywords】anomaly detection, self-organizing maps, SCADA, wind turbine【Author】
|Mian Du||: China Electric Power Research Institute, Beijing, China;|
|Shichong Ma||: China Electric Power Research Institute, Beijing, China;|
|and Qing He||: China Electric Power Research Institute, Beijing, China;|
【出版时间】Tue Aug 09 08:00:00 CST 2016
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