# Enhancing Reliability in Wind Turbine Power Curve Estimation
## 4 Discussion  
We want to point out different aspects. In the context of this work, we understand by anomalies all those points that, due to failures in the sensors that measure the power or the wind speed or due to the starting and stopping processes of the WTs, fall in places far from the theoretical curve. Therefore, to obtain a good curve estimation, we note the need to filter anomaly data before the estimation to remove anomalies, as otherwise supervised ML-based methods also attempt to capture them into the models. So, when working with \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Hidden Layer Size:** & **5** & **7** & **10** & **15** & **20** & **25** \\ \hline WT80 & 82.57 & 82.59 & 82.44 & 82.45 & 82.40 & 82.40 \\ WT81 & 75.36 & 74.90 & 74.84 & 74.87 & 74.84 & 74.85 \\ WT82 & 77.73 & 77.35 & 77.37 & 77.32 & 77.28 & 77.42 \\ WT83 & 72.63 & 72.59 & 72.58 & 72.53 & 72.56 & 72.82 \\ WT84 & 70.66 & 70.69 & 71.18 & 70.61 & 70.58 & 70.54 \\ mean: & 75.79 & 75.63 & 75.68 & 75.56 & 75.53 & 75.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: RMSE in the training (experiment 2). Figure 14: Representation on the SCADA data (points in lighter blue) of the WT80 of the modification of the filtering rule by changing the manufacturer’s curve to the power curve (darker blue) estimated with an ANN of 5 hidden nodes. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Hidden Layer Size:** & **5** & **7** & **10** & **15** & **20** & **25** \\ \hline WT80 & 247.96 & 247.94 & 247.91 & 247.88 & 247.88 & 247.88 \\ WT80 & 235.62 & 235.68 & 235.71 & 235.71 & 235.68 & 235.45 \\ WT80 & 323.82 & 323.83 & 323.80 & 323.99 & 323.90 & 324.01 \\ WT80 & 309.51 & 309.35 & 309.59 & 309.64 & 309.37 & 309.97 \\ WT80 & 392.62 & 392.39 & 392.95 & 393.58 & 393.03 & 392.53 \\ mean: 301.91 & 301.84 & 301.99 & 302.16 & 301.97 & 301.97 & 301.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: RMSE in the test (experiment 2). SCADA data, estimating the power curves of each WT from the data (cleaned of anomalies) is necessary, since there are significant differences from the power curve provided by the turbine manufacturer. The cause of such significant differences seems to be the nature of the SCADA data, which consists of temporal averages, in our case, of 5 minutes. Instead, the manufacturer's values model instantaneous relations of wind and power. In this work, we use a filtering rule that depends on the choice of \(w_{off}\) and \(p_{off}\) values, which can slightly determine the estimates of the curves in the regions where the curve meets the flat areas. In any case, what is vital is to consider the same rule in both the training and test phases to clean anomalies in the same way. This rule can be refined by changing the estimate of the curve provided by the manufacturer, \(P_{7}(w)\), to the estimate of the curve measured in each WT. As noted in our work, we have kept a general rule for all WTs. Once the estimates are made, we see that, despite presenting significant similarities, each WT has its particular curve. In this sense, it is also interesting to see that, for each WT, the estimates made with the training and test data are highly similar for all the WTs that work correctly and for the one we know has a failure in the test part, the WT84, the malfunction is captured in its curve. The curves (to be precise, the points provided by the manufacturer) have been obtained according to a measurement protocol that is very clearly defined to be comparable between WTs from different manufacturers. Taking measurements under these conditions is expensive, so the manufacturer only provides points every 0.5 m/s. We understand, therefore, that the measurements supplied by the manufacturer are exact. In working conditions, things change slightly. First, of all, environmental conditions are continuously evolving. For example, it is known that air turbulence affects the generation conditions or that sometimes the orientation of the blades is not optimal either.