Wind Turbine Anomaly Detection Using Mahalanobis Distance and SCADA Alarm Data
Jui-Hung Liu, Nelson T. Corbita, Rong-Mao Lee, Chun‐Chieh Wang
Abstract
Wind energy is becoming a common source of renewable energy in the world. Wind turbines are increasing in number, both for onshore and offshore applications. One challenge with wind turbines is in detecting anomalies that cause their breakdown. Due to the complex nature of the wind turbine assembly, it is quite an extensive process to detect causes of malfunctions in the system. This study uses the Mahalanobis distance (MD) to detect anomalies in wind turbine operation, using SCADA alarm data as a comparison. Different predictive models were generated as the bases for analyses in MD computations. Using the SCADA alarm data as a reference, trend patterns that deviated from the threshold value were compared. Results showed that the MD could be used to detect anomalies within a group of data sets, with behaviors learned based on the model used. A large portion of those data sets deviated from the threshold level, corresponding to serious alarms in the SCADA data. We concluded that the MD can detect anomalies in different wind turbine components, based on this study. MD analysis of models can be used in conditions monitoring systems of wind turbines.