# Detection of Wind Turbine Failures through Cross-Information between Neighbouring Turbines
###### Abstract
Next, the data must be accurately filtered and often enhanced by generating additional features that can highlight interesting features in the data. Finally, a classification model can be trained and used for prediction. All these steps are time-consuming. Label assignment is most likely the most important and the most time-consuming step. Work orders and alarm logs can be used to assign labels, but the lack of a standard format, the free-text nature of the data, and common discrepancies in the information make it difficult to automate processes and ensure that labels are reliable. Regression requires a less complex pipeline, filtering data through labels rather than defining groups. A common challenge in regression tasks is to choose a distance metric to use to quantify the quality of predictions and to assess the difference between predicted and observed values when analysing new data. Analysing the results, choosing the number of clusters, and interpreting them is important and non-trivial, but clustering and anomaly detection are much easier to train and easier to pre-process the pipeline. In general, the methods for condition monitoring with SCADA data use several strategies such as signal trending, modelling with neural networks, or some physical models. To conduct some form of exploration or analysis of the data, machine learning-based algorithms are commonly used. For example, in [12] the authors use machine learning on SCADA data to predict WT failures. However, because the scenario is very unbalanced, with most of the samples coming from one class, such classifiers tend to perform poorly [13]. For this reason, the authors in [4] propose to use normality models to predict a (real) variable from a subsystem based on the rest of the variables and comparing them with the actual variable to determine if the predicted variable was consistent with the real one. If the predicted diverged from the real one, this divergence indicated a deterioration of the sub-system. In this initial work, Extreme Learning Machines (ELM) were used for implementation, while the results were compared with other widely used methods, such as Partial Least Squares (PLS), Support Vector Machines (SVM) or Deep Artificial Neural Networks (DANN). ELMs were selected because they can be trained very easily and fast, which is a very interesting feature. If the system has a fast training stage, the parameters can be adjusted very rapidly so that the models can be adjusted and fine-tuned in a very short time. Interested readers can find more details in [4]. The SCADA system usually collects data at 10-min intervals. However, other intervals are also possible, such as 5 min. Recently, some work has investigated the effect of sampling frequency when collecting data in SCADA systems. For example, in [14] a wind turbine performance monitoring technique based on high-frequency SCADA data is presented. The sampling frequency is 4 s and it is shown that its use is beneficial for performance monitoring, because the use of 5-min data tends to smooth this variation and does not provide the degree of detail provided by high-frequency data. [PERSON] and [PERSON]'s suggestions in [15] also highlight that 5-min data is not appropriate as a standardised solution. Nevertheless, it is a valuable and relevant tool for the development of condition monitoring systems (CMS). High frequency data appears to be a good option for CMS development and to bring more information for wind farm management, as pointed out in [16]. In any case, ANNs of different types have shown their usefulness in the construction of these regression models. The idea behind it is simple. Periods of good operation of the machines to be modelled are determined and a model is built that predicts one or more outputs (targets) from other input variables. The most direct way is to build a regression system that predicts the targets. Once the model is trained, it is kept running in the test part. The error between estimation and measurement gives an idea of how well the system is working.