# Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines
## 4 Conclusions  
Predictive maintenance of wind plants exclusively through SCADA data is a complicated task because wind turbines are made up of complex systems/subsystems that work in a variety of operating regimes. That is why it is a challenging task which is still in progress, but that can represent a very big competitive advantage over other O&M options that involve the installation of extra equipment, since data collection is simple and cost-effective by taking advantage of the sensors already present in the WT. Moreover, the processing of these data can be done in the cloud, activating economies of scale in such a way that the monitoring of many plants can end up in specialized centres that offer their services at a competitive cost and using state-of-the-art technology. In the wind parks the SCADA system collects, every 5 min, a vector of points corresponding to the values of the sensors used to predict the value of the target variable. When the system is working properly, the scatter plot representation (target, predicted value) will fall very closely on the 45-degree line. Because a new point is available every 5 min, 12 new points are accumulated in 1 h. With the set of accumulated points (obtained in a few hours), the regression line can be drawn. If it works properly, this regression line will fit very well to the 45-degree line. In the event of deterioration, the regression line will change its slope slightly. The decision, therefore, is never based on a single point. The more points used to draw the regression line, the more reliable the result. The park manager should establish the number of hours and the degree of change considered in order to decide how and when the turbine needs to be checked. The ELM model is chosen because beside its simplicity, it solves the overdetermined system through the inverse of Moore-Penrose, providing the solution that approximates the points with the Euclidean minimum norm. An additional advantage is that with very few changes we can build models to estimate multiple variables or a single one and we can obtain estimates of multiple variables directly for the whole wind farm. In this work we have shown that ELM provides the solution that approximates the points used in the test by getting the minimum quadratic error. This fact is an advantage when training the models because it avoids the overfitting observed in more sophisticated methods, such as SVM or DANN. It is also robust against possible wrongly-labelled data that can be introduced in the training phase due to the impossibility of correctly filtering the whole database. In the experiments, we have shown that normality models built using ELM are adequate to detect possible malfunctions of the wind turbines up to 12 days in advance. With the set of points generated by a turbine during a day, regression lines can be drawn that are adjusted to 45 degrees very accurately when the system under testing is working correctly. Therefore, under these conditions, deviations in the slope of the line can generate a warning or an alarm. The threshold for deciding when it is a warning or an alarm will depend on the park and the strategy of the managers, who may be more or less conservative in their decision to send a technician to supervise the turbines. In addition, other factors must be taken  
Figure 15: 3-dimensional (**a**) and 2-dimensional (**b**) space of the pressure measurements. into account, such as the variance of the error and, very importantly, the range of value of the variables and the operational state of the turbine, as it could be inactive. All this is important for a correct interpretation of the results. Despite its simplicity, the ELM achieves the best performance amongst the compared models. Having an extremely efficient, low-cost computational model training approach such as ELM allows for the performance of many tests, and also lets us apply a parallel grid-search strategy on a wide set of parameters.