# Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines
## 2 Materials and Methods  
### Model of Normality  
In previous works [13; 14] we have applied ML techniques in the field of predictive maintenance of wind turbines and have been doing it from a classification point of view. The objective was to determine the health-related states of the turbines and their different systems and/or subsystems, paying special attention to pre-fault states, which we want to detect in the turbines we test. The main problem encountered with this strategy is the labelling of the data in terms of the health states. Obtaining correctly-labelled data is a very complicated step. Occasionally, a turbine can be found that has suffered a certain type of failure where the data is easy to label when referring to the health of some of its subsystems, but in a general way the labelling is a complicated process that needs a lot of human supervision and expert decision-making. Leaving aside the fact that different experts may differ in their labelling criteria, it is necessary to deal with the fact that there are few cases of failure in each subsystem, and furthermore they are difficult to extrapolate between wind turbines from different manufacturers, operating in different parks and in a regime of conditions that can vary greatly.  
However, we have a lot of data from turbines and systems that work well, so it is feasible to establish a model of normality by means of regression strategies. For a given subsystem, the strategy consists on predicting a variable using the rest of the variables of the subsystem and detecting that it is out of normality in the event of a breakdown. Once again, here experience in choosing the variable is very helpful. The advantage of this strategy is that the models can be trained with all the turbines in the park and with data corresponding to all the operating modes. The training is carried out on the first half of the data from all the turbines which are working correctly, and although it is possible that some incorrectly labelled points are present in some cases, the over-representation of the cases where the turbines are working well makes the model very precise. The effectiveness of this strategy depends on: (i) having a system capable of predicting the model's variable with maximum effectiveness (minimum RMSE) when being in the normal (healthy) state; and (ii) with enough sensitivity to detect deviations in this variable when the predictions begin to fail due to the deterioration of the systems. This phenomenon is observed by exploring small time windows of data. In the regression lines of the normality state, this phenomenon is observed in a change of slope of the line and in the dispersion of the predicted points, which also move away from the 45-degree line. Because it can be observed before the failure occurs, the health status of turbines can be foreseen, performing what is therefore known as a prognosis.  
Summarising, the key point of the method that will be presented in this work is to train a model using data collections recorded in different operating regimes when the turbines were working properly. For the model to be accurate enough, it is important to emphasise that the records must be sufficiently rich and varied to represent different wind and weather conditions. The underlying idea is that when the system is operating correctly, the estimation and measurement of the target signal follow the trained model and are practically the same, so that the plot of the measurements against the estimates is a 45-degree line. However, when systems deteriorate, this line is disturbed, thereby changing the slope as measurements and estimates begin to diverge.