## 1 Introduction  
Energy is one of the most important aspects of the evolution of our society. Easy and cheap access to energy has always been a challenge, and nowadays there is the added factor of pollution that can be caused by the type of energy source employed. Increasingly, carbon and gas-based energies are being restricted in favour of green energies, gathered from the sun, tides, or wind, to name but a few. These renewable energy sources are an excellent choice because they are clean and can exist, in different forms, over a wide geographical area. This is another important point in democratising access to energy, as for many countries it is not possible to access carbon-based energy. One of the greatest financial effects on the Levelised Cost of Energy (LCOE) of wind turbines (WT) is linked to the operations and maintenance (O&M) tasks. Various systems have been proposed to decrease the O&M costs. In [1, 2, 3], the reader can find an overview of several strategies that could be used. One of the options is to detect, as soon as possible, abnormal behaviour in WTs, before failures occur. Then, because the missfunction can be solved faster and without important damages on the WT subsystems, the LCOE is reduced. There are various ways to deal with that, and in this work a data-driven Normal Behaviour Model (NBM) will be used, based on our previous work [4], exploring how the temporal scale affects the results of the prediction system. Typically, WTs have a SCADA system that provides a set of data to monitor the status of the subsystems [5]. This data comes from many sensors placed in different parts of the WT, and comprises the minimum value, maximum value, mean value, and standard deviation of various physical measurements, such as temperature, pressure, voltage, current, etc., in various parts of the subsystems. Generally, the methodologies for condition-monitoring using SCADA data are based on signal trending strategies, artificial neural networks, and physical models. Machine learning algorithms are habitually used to perform some type of exploration and/or analysis on the data. This can be done, for example, for predicting failures in wind turbines using SCADA data [6]. The problem linked with using classification models to predict failure states are related to the extremely unbalanced scenario, in which almost all the examples are from one class (healthy state) and very few examples from the other class (alarm state) [7], in addition to the errors present in the labels of the data, which make it even more complicated to use it, unless a huge human expert-based pre-processing is carried out beforehand. This is why the use of normality models was proposed in [4] for predicting one variable generated by a subsystem, using the rest of the variables, and comparing it with the real value in order to detect if the prediction is out of the norm, in which case that will indicate that the subsystem deteriorates. In this first work, the model was implemented using extreme learning machines (ELM), comparing the experimental results with other well-known machine learning approaches such as partial least squares (PLS), support vector machines (SVM), or deep artificial neural networks (DANN). One of the reasons to use ELMs is the speed of training, which is done quickly by calculating an inverse Moore-Penrose matrix and simple matrix algebra manipulations. Having a fast training system enables parameters to be tuned very quickly, so that models can be tested and adapted in a very short time. Details on the ELM algorithm and its implementation can be found in [4]. The information gathered by the SCADA system is habitually provided at a frequency of 10-min intervals, but in some cases the system reports values every 5 min, obtaining 6 or 12 samples every hour, respectively. Recently, some works have investigated the effect of the sampling frequency in SCADA systems. For example, in [8] a technique for wind turbine performance monitoring based on high-frequency SCADA data is presented.