# Wind Turbine Anomaly Detection Using Mahalanobis Distance and SCADA Alarm Data
## 2 Materials and Methods  
The general data of the wind turbine from the wind farm used in the study are presented in Table 1. It should be noted that in using the SCADA system of the wind turbine, knowledge of the different characteristics of the wind turbine is essential. Knowing the different characteristics of the wind turbines used in the study can help ensure accuracy in the conclusions generated. For this study, a direct-drive onshore wind turbine located in Taiwan was used. A flowchart presented in Figure 2 is used to accomplish the study's objectives. It begins with the extraction of operational and alarm data from the SCADA system. These data are then pre-processed to filter abnormal data underlying different periods. The pre-processed data are then used to generate predictive models using the algorithm presented in [23]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Data** \\ \hline Location & Taiwan \\ Number of wind turbine heads & Fourteen (14) \\ Nominal power capacity of wind turbine & 2.0 MW \\ Transmission connection & Direct-drive \\ Number of parameters in SCADA system & Thirty-two (32) \\ Number of component models generated & Seven (7) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: General data of the wind turbine and SCADA system from the wind farm used in this study. Figure 2: Flowchart used in the study. Table 2 shows the specifications of the models that use neural networks. Based on the SCADA parameters used, seven output component models were generated. Using the different models generated from the predictive model algorithm, the MD was calculated between predicted and actual values. In the generation of the predictive models and the calculation of the Mahalanobis distance, the computing programming software MATLAB was used. The MD for the ten-minute duration points for anomaly detection was averaged for the first twelve hours of the day and for the following twelve-hour points. The method employed provides two averaged data points for each day. These averaged data points are then plotted for the entire year or duration of the study to show abnormalities based on the threshold value. The threshold value for the study was calculated based on the typical characteristics of the data points [24]. The healthy data of the SCADA system was used in calculating the threshold value using the same MD approach. The averaged MD value was used as the threshold value. In order to backtrack the estimated date of occurrence of the anomaly, reverse engineering was used. The point or time of the year where the anomaly occurred was divided by two, and this value represents the date in the year of the occurrence. The time of the year was divided by two, since the averaging of the MD distance was done twice a day. Calculating this would result in the estimated date of occurrence of the anomalies. Anomalies detected with the MD approach were then compared with the SCADA alarm data for the duration of the study. The component of concern was also part of the comparison with the SCADA alarms. The different input parameters were also considered in comparison with the SCADA alarms. In other words, the input parameters were checked to see if they coincided with the SCADA alarms. Table 3 shows the list of these input parameters for each output model used in the study. The different parameters were combined with their correlations to the output parameters [25]. Using MATLAB software, the different input and output parameters were used for the generation of the specified model. This ensured that these input parameters were related to the output parameters in terms of data behavior.