# Detection of Wind Turbine Failures through Cross-Information between Neighbouring Turbines
## 6 Conclusions  
The set of SCADA variables of all the turbines in the wind-farm operating correctly follows a well-defined variation pattern group, especially by the variables of the type_avg. When one of the turbines malfunctions, or stops working, its variable set is decoupled from the general pattern. These two facts allow a comparison of the health status of the turbines. To do the comparison, a reference signal is calculated, corresponding to the median value of the signals of each turbine. The underline idea is that WTs will hardly fail all at the same time, so that the detection of the malfunction of one of them can easily be done via the difference of their signals with the reference signal. In this first exploration it can be seen that the residuals, the difference between the signal from each turbine and the reference signal, have an interesting and powerful potential to generate warnings and that most of them are related to the status of the wind turbine. Moreover, the detection of a fault does not only appear in the signals associated with the subsystem that fails, but also transversely in the rest of the variables in a very general way. Hence, the detection of abnormal behaviour does not depend on the exploration of a single signal. An advantage of the presented technique is its explainability, as we can see why the peaks are generated in the residue. In the present format, the representation of the signals can already provide a visual idea of the occurrence of potential problems. In fact, an analyst can study and interpret the signals causing the alarm and assessing the risk before a failure. Another advantage is that once the strict time alignment of all the data of all the signals of all the WTs has been verified, the information can be stored in tensors (multidimensional matrices), which can be of third order, where the dimensions can be _time_\(\times\)_signals_\(\times\)_WT. The operations involved, such as filtering outliers, can be performed very quickly on this structure. Similarly, reference signals or patterns can be calculated by performing the variance operation, operating only in the third dimension of the tensor. This means that with very simple operations, the residuals of all the signals of interest of an entire park can be generated massively and automatically without the need to train models. Compared to supervised systems, this is a very important advantage. This method could also be easily modified and used to mark the signal sections where WTs are stopped to better refine the signal sets that are selected.This would allow better labelling of the signals used in the training phase, e.g., by discarding the sections where WT stops, if one wants to train a normality model for the forecast. It should be noted that the strategy presented is simple and allows a massive analysis of all the signals, being easily parallelised and with a very controlled computational cost. However, as with all methods, the proposed method has some possible drawbacks. One of the disadvantages is the need to introduce a threshold on the residual signal when generating alarms automatically. This disadvantage is common with the normality models based on regressors that also need to apply a threshold on the error signal when generating an alarm automatically. At this point, operator supervision would be desirable. In the work presented, there have been a total of 13 possible alarms (the rectangles in Figures 12-16) over an interval of almost 2 years, which represents an easily manageable volume. Another disadvantage in presenting this paper is that it is a seminal work and there are no other works on systems using SCADA signals with information from various WTs in the wind farm available for comparison. It will therefore be interesting for future work on other data to use the proposed method and confirm its effectiveness. conceptualization: [PERSON] and [PERSON]; methodology: [PERSON], J.C. and [PERSON]; software: [PERSON], F.J.L. and [PERSON]; validation: J.C., [PERSON] and C.F.C.