# Wind Turbine Anomaly Detection Using Mahalanobis Distance and SCADA Alarm Data
### Related Studies  
One study on the statistical evaluation of SCADA data for condition monitoring indicates that there are advantages and limitations of using SCADA in condition monitoring [10]. One such limitation is in its capacity to detect anomalies by itself. SCADA data are limited to the operational values of the wind turbines which can not function for anomaly detection alone. Statistical analyses can be conducted in order to address this limitation, and one tool that can be used is the \"Mahalanobis distance\". Based on the characteristics of the Mahalanobis distance, its applicability in fault detection for other applications has already been studied. One study made use of the MD approach in detecting incipient sensor faults. In this study, the model used the MD approach to compare with a conventional approach. The results showed that the MD approach's performance was comparable with the conventional one [11]. It was emphasized in this paper that the MD approach is a data-driven monitoring method. Another study that used the MD approach in detecting anomalies involved detecting multivariate outliers in a data set. Multivariate outliers can cause disruptions in the data's behavior, resulting in erroneous results. This led to the application of the MD approach to detect these outliers in the data set. The study results showed that the MD approach was able to detect multivariate outliers, even though the accuracy was relatively low compared to other methods [12]. A study in [13] conducted an incipient fault diagnosis using the MD where the detection was based on empirical probability estimation. The results from the simulation showed that the proposed methodology was effective for non-Gaussian data, and at the same time sensitive for incipient fault detection. The case study presented also highlights the benefits of the proposed methods compared to state-of-the-art-based solutions. The MD approach was also used in methane detection using spectroscopy. The study aimed to incorporate the MD approach in gas detection and calculate the corresponding effects of errors in the detection method. Their results showed that the error was minimized for the methods that incorporated the MD approach [14]. A better error detection performance was achieved when the MD approach was incorporated into the anomaly detection method. In terms of anomaly detection in wind turbines, several methods using data analysis were developed. The paper in [15] developed an anomaly detection method based on SCADA data mining. This method extensively used the data in the SCADA to create correlations between abnormalities and performances of wind turbines. The results showed a promising use of SCADA data in detecting abnormalities in wind turbines. Two studies in [16; 17] proposed new methods that can be used for wind turbine diagnosis using SCADA data. Parameter features from the SCADA data were used in [16], while vibration measurements in the tower were used in [17]. Both of these methods were  
Figure 1: Illustration of the Mahalanobis distance in a bivariate space adapted from [9]; the contours represent the behavior of the MD in the bivariate space. The Mahalanobis distance can detect outliers in the bivariate space. seen to be promising in developing future wind conditions monitoring systems. Another paper [18] showed how SCADA data for a gearbox was used in creating a new anomaly detection technique. The paper used machine learning algorithms in the creation of a model that was used for anomaly detection. Compared to other techniques, this technique was able to reach a comparable efficiency. Using SCADA data in anomaly identification was emphasized in another article [19]. In this study, an improvement in the deep belief network was used as the basis for anomaly detection. Results showed considerable efficiency in terms of accurately detecting abnormalities in wind turbines. These papers used SCADA data and analyzed them in a variety of ways to create an abnormal detection method.