# Wind Turbine Anomaly Detection Using Mahalanobis Distance and SCADA Alarm Data
###### 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.  
wind turbine; Mahalanobis distance; anomaly detection; SCADA alarm; predictive models +
Footnote †: journal: Applied Sciences  
will be created and used for the data analysis using the Mahalanobis distance approach. A comparison with SCADA alarm data will verify the model's performance using the MD. The operational data used in this study were extracted from a wind farm in Taiwan with fourteen wind turbines, during the year 2019.  
The focus of this study is the development of an anomaly detection method for wind turbines using SCADA operational data and the MD approach. Since this study is still in the developmental stage, our approach was not yet integrated into a wind turbine's actual CMS. Furthermore, since different parameters are involved in different wind turbine SCADA designs, this means that minor changes in this approach should be applied when testing other wind farms [6]. Furthermore, the wind farm tested in this study is an onshore, direct-drive type system.  
This study will use the Mahalanobis distance to measure distance variations in the data set generated from predictive models. This measure can provide insights into the duration of the generated data where abnormal behavior can be observed. In this way, abnormalities in the wind turbine can be checked using the SCADA alarm data generated throughout the study.  
In this study, anomalies detected by the MD method were analyzed based on the SCADA alarm data. No count or measure of the frequency of various anomalies was conducted. This means that there was no further analysis conducted in terms of the frequency of alarms that were generated during the duration of the anomaly detected from the MD analysis.  
The following sections describe the Mahalanobis distance, which is the main approach used in this study, followed by a summary of related research focused on the use of the MD in fault detection and SCADA systems for conditions monitoring of wind turbines. A description of the wind turbine data used and a description of the proposed method are then detailed in the Materials and Methods section. This is then followed by a discussion of the results and the conclusions, which address the future scope of this research.