# Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines
## 3 Results
### Prognosis Experiment  
Wind Turbine Failure Prediction is a very complicated task due to the previously mentioned problems of wrong labelling and unbalancing data. The results of the classification models that have been applied before in this wind farm using SCADA data are very good in the training step. However, when the models are tested on new data, they generate a huge proportion of false positives. To give an idea, it is quite hard to reach a Kappa value higher than 0.1, and it is common to obtain values far bellow. Moreover, classification models neither explain the meaning of the classification. All this makes classification very tricky. This is why now we present a strategy to perform wind turbine failure prediction based on normality models and regression. Since we have access to the alarm records we can study the behaviour of the model in relation to the different turbines in the preceding moments to each of the alarms. For this experiment, the alarms recorded from the gearbox subsystem were selected. Out of the possible alarms associated with this subsystem, those that are present in the history of each of the turbines were checked. In Figure 13a, the time distribution of the five types of alarms is depicted, corresponding to the WT#84 turbine. These five types of alarms correspond to the indicators id_1271, id_1369, id_1544, id_2302 and id_2306, described in Table 4. The experiment consists of grouping all these alarms, similarly to what is represented in Figure 13b. Once the alarms have been grouped, without distinguishing between them, the pair \((target,\hat{y})\) is represented by the points corresponding to the time interval 12h before each of these alarms occurred. As expected, when the turbine works according to the model, the points \((target,\hat{y})\) fall very close to the 45-degree line. The goal is to detect, with only this small set of data, if the model has the ability to anticipate the failures and capture points outside of the line. \begin{table}
\begin{tabular}{l l} \hline \hline
**Alarm Code** & **Description** \\ \hline id\_1271 & MGB Fuse TripCoolWatPp, Trip of the water pump’s fuse in charge of cooling the gearbox oil. \\ id\_1369 & MGB Temp OilSump \textless{} SL, The temperature of the oil sum is below the expected result for a operation regime. \\ id\_1544 & PT100 defective, temperature sensor PT100 defective. \\ id\_2302 & MGB TempBear150 \textgreater{} SH, The temperatue of output high speed bearing super high. \\ id\_2306 & MGB Temp ErrTimeLimit, The timeout at reading the gearbox temperature sensors. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Description of the error codes. Figure 12: Test of the selected model (\(H=92\)) for the WT#84. (**Left**) the regression plot using all the test data. (**Right**) from a temporal window where the target \(\hat{y}\) (**top**) and the error (**bottom**) can be observed in more detail. The WT#84 has many points out of the 45-degree line. Different behaviours are captured with respect to the model, as can be seen for the selected time frame in Figure 13c. The mismatch is observed for both regimes of operation, corresponding to the high part of the line (target above 60) and for the low part of the line (target below 30). With respect to the other turbines, for WT#80 (Figure 14a) and WT#81 (Figure 14b) the test also captures distant (discordant) points, especially in low operating regimes (target below 30). On the other hand, for WT#82 (Figure 14c) and WT#83, the malfunction, if any, is more difficult to detect for this portion of data considered. Figure 14: Representation of the pair \((target,g)\) generated by the model using the data corresponding to the intervals that go from 36 h to 12 h before each alarm is produced for the WT#80 (**a**), WT#81 (**b**), WT#82 (**c**) and WT#83 (**d**). Figure 13: (**a**) Temporal distribution of the appearance of alarms in the gearbox subsystem to the WT#84. There are 5 different alarms (indicated by the identifiers of the Führhlander).