# Wind Turbine Anomaly Detection Using Mahalanobis Distance and SCADA Alarm Data
## 3 Results and Discussion  
The Mahalanobis distance for each model and wind turbine is plotted for the entire duration. Figure 3 shows the results of these plots for the brake hydraulic pressure model for all the wind turbines in the wind farm. The plot shows some abnormal behavior for some wind turbines. An example would be wind turbine H11, where abnormal behavior can be seen at the end of the year. The different output models show different durations or periods of occurrence of abnormal behavior, and these are subject to comparison with the SCADA alarm data. \begin{table}
\begin{tabular}{c c} \hline
**Output Model** & **Input Parameters** \\ \hline \multirow{5}{*}{Generator speed} & average alternator bearing temperature \\  & generator voltage \\  & generator current \\  & active power \\  & average generator air gap temperature \\  & average alternator bearing temperature \\  & generator current \\  & active power \\  & average generator stator temperature \\  & generator speed \\  & average generator air gap temperature \\  & generator voltage \\  & average inverter current \\  & average inverter voltage \\  & cooling water low pressure \\  & active power \\  & blade 2 motor current \\  & blade 3 motor current \\  & average blade angle \\  & active power \\  & XY direction resultant force vibration \\  & X direction vibration \\  & Y direction vibration \\  & active power \\  & average generator stator temperature \\  & generator current \\ Active power & average inverter current \\  & rotor speed \\ \hline \end{tabular}
\end{table} 
Table 3: List of input parameters for each output model. The results show that abnormal behavior can be detected in the calculated MD for the data set. This shows the ability of the MD to detect faults in a component where data is extensively used, and where data characterizes the system's performance [27]. The expected behavior of the data set shows slight variations, while portions with faulty behavior show widespread deviations with the rest of the data set. Using the plotted results in Figure 3 can help assess the wind turbine behavior for different parameter data sets. Observing the plot can help one infer the different wind turbines where clear abnormal behavior in the data set was generated, based on the MD measure. Thus, this plot can be useful for generating observations that can lead to the detection of anomalies based on the MD measure. An example would be a plot based on the brake hydraulic pressure model where the MD measures for the different wind turbines are shown. It can be noted that different behaviors can be observed for the wind turbines in the wind farm. This observation can also be used in implying the efficiency of the generated models in going deep into the predictive models. However, this is not covered in this study. As shown in the plot for the MD measure of different wind turbines for the brake hydraulic pressure model, certain observations can be generated for durations where there is abnormal behavior. A good observation example is the end part of the year for wind turbine H11, where there is a clear abnormal behavior in the data set. This will then be  
Figure 3: Plotted Mahalanobis distances based on the brake hydraulic pressure model of some wind turbines in the wind farm. tested using the threshold value generated from the normal behaviors of the data set. This shows that having a summary of plotted MD measures for different wind turbines of different predictive models can be useful in making observations that may lead further to anomaly detection. The brake hydraulic pressure model for wind turbine H11 shows a period where abnormal behavior is detected. Figure 4 shows this period emphasized inside a dashed red box within the plot. The thick threshold line shows within the plot which data are in typical or faulty behavior. The period of occurrence of the faulty behavior is calculated by tracing the period corresponding to the day of the year of the occurrence.