A SCADA Data based Anomaly Detection Method for Wind Turbines
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.
anomaly detection self-organizing maps SCADA wind turbine
Mian Du Shichong Ma Qing He
China Electric Power Research Institute,Beijing,China
国际会议
西安
英文
1-6
2016-09-01（万方平台首次上网日期，不代表论文的发表时间）