# Wind Turbine Anomaly Detection Using Mahalanobis Distance and SCADA Alarm Data
## 3 Results and Discussion
It should be noted that the day of the year presented in the plot should be divided into two, since the averaged MD is calculated twice a day. Table 4 further looks into the MD value and time-stamp around the SCADA alarm region. It can be observed that the highest MD value of 6.42 occurred at time-point 645, which means a time period between 19 November 2019 00:00 and 19 November 2019 12:00. The different SCADA alarm data are then checked for correspondence with the component where the MD faulty behavior is detected. Relevant data in Table 5 show that alarm data for November for wind turbine H11 indicate a very low pressure on the yaw brake system. This corresponds to the detected anomaly in the calculated MD of the brake hydraulic pressure model. In terms of the component analysis of the different parameters, the MD approach detected the faulty behavior of the wind turbine. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Time-Points of the Year** & **Real Time-Stamp** & **MD Value** \\ \hline
641 & 2019/11/17 00:00 & 0.077828383 \\ \hline
642 & 2019/11/17 12:00 & 0.200583207 \\ \hline
643 & 2019/11/18 00:00 & 1.202948234 \\ \hline
644 & 2019/11/18 12:00 & 2.032181356 \\ \hline
**645** & **2019/11/19 00:00** & **6.421187127** \\ \hline
646 & 2019/11/19 12:00 & 1.909682528 \\ \hline
647 & 2019/11/20 00:00 & 0.221972715 \\ \hline
648 & 2019/11/20 12:00 & 0.273416611 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Detailed MD value and timestamp list around the SCADA alarm region for H11. Figure 4: Mahalanobis distance based on the brake hydraulic pressure model for H11. Another example is based on the stator temperature model plots for the calculated MD. Wind turbine H07 shows faulty behavior among the plots in the wind farm for stator temperature. Figure 5 shows the plot for the calculated MD for the stator temperature model for H07. Tracing the period of occurrence for this abnormal duration results in October. The SCADA alarm data for wind turbine H07 shown in Table 6 shows a very high temperature in the generator stator and bearing temperature fault for October. It should be noted that bearing temperature is part of the input parameters for the stator temperature models. In terms of parameters in different components, the MD approach was able to detect the faulty behavior of the wind turbine. In summary, calculating the percentage of the detected faulty behaviors based on the SCADA alarm data shows that over 97 percent of the faulty behaviors were detected using the MD approach based on different models of the wind turbines in the wind farm. This accuracy is comparable to the studies presented in [28; 29], as shown in Table 7. These papers used anomaly detection methods involving data analysis and machine learning methods. They were able to generate efficiencies of more than 95%, which is comparable to the efficiency generated in this study. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Wind Turbine** & **Alarm Code** & **Alarm Description** & **Date/Time** \\ \hline H07 & 249 & generator stator temperature very high & 9 October 2019 10:56 \\ H07 & 242 & bearing temperature fault & 4 October 2019 11:40 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Part of the SCADA alarm system corresponding to anomalies for H07. Figure 5: Mahalanobis distance based on the stator temperature model for H07. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Wind Turbine** & **Alarm Code** & **Alarm Description** & **Date/Time** \\ \hline H11 & 228 & Yaw Brake Pressure Very Low & 19 November 2019 4:02 \\ H11 & 228 & Yaw Brake Pressure Very Low & 19 November 2019 17:15 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Part of the SCADA alarm system corresponding to anomalies for H11. Results of the plotted MD for each wind turbine model show that abnormal behaviors can be detected based on the threshold of the data set. The characteristics of the Mahalanobis distance can be used to detect faulty behaviors in the wind turbine and can be integrated into the condition monitoring system.