###### Abstract
Different algorithms, as well as the introduction of context variables, have been studied in order to get a reliable picture of the turbine behavior. The main drawback of this approach is its incapacity to determine which component is causing underperformance since the turbine is studied as a whole. Solutions based on control monitoring systems (CMS) are available and have been studied in the literature [9; 10; 11]. These analyses typically use vibration, sound and acceleration measurements to detect anomalies in the behavior of bearings, gearboxes and other mechanical components. The frequency of the data used for these analyses is much higher than the typical SCADA data, thus bringing more information for the detection of failures. That being said, most turbines are not provided with vibration sensors, the installation of these instruments disrupts the operation of the turbine and can cost a windfarm owner thousands of euros per turbine. The authors of Reference [12] presented a thorough analysis of the available monitoring techniques for wind turbine; regarding the CMS, they highlighted as main challenges: financial cost, difficulty of interpretation of the results and not-trivial integration with all the existent monitoring systems, as well as its scalability. For these reasons, solutions based on the usage of SCADA data can be particularly interesting for owners of old turbines, since no installation of additional sensors or interruption of their operations is needed. Value can be created from the large quantity of unutilized SCADA data stored in their databases. The rapid growth of the Deep Learning field led many researchers to apply neural networks to solve data challenges. Autoencoders in particular appear to be a good fit for anomaly detection. Autoencoders have been applied in multiple practical applications, such as anomaly detection of seasonal Key Performance Indicators (KPIs) in web application [13], cyber-security monitoring [14] and monitoring of gas turbine conditions [15]. In the wind energy sector, [PERSON] et al. stacked multiple autoencoders to extract new representations of vibration data in the event of gearbox failures [16]. Successively, they also utilized denoising autoencoders, enriched with temporal information to assess turbine conditions in a laboratory and online scenario [17]. Finally, autoencoders have been successfully used for ice-detection on turbines' blades by [PERSON] et al. [18]. Alarms and events records have been used to determine the remaining useful life of wind turbines [19]. In Reference [20], the time-sequence of the alarms is analyzed to detect relations between the different alarms, determining the causal relationship between the different events and helping to determine the root-cause of failures. This research aims to explore the capabilities of autoencoders and SCADA alarms as a hybrid fault detection system for wind turbines' generators. While in the literature examples of predictive strategies based only on SCADA data or alarms are present, no holistic approach using both sources of information is present. This paper reports a methodology that takes advantage of both SCADA and alarm logs in the same algorithm. As a benchmark, other typical anomaly detection algorithms are implemented, and their results are compared with the autoencoder's results. Additionally, the overall methodology is compared to a normality model, one of the most common predictive maintenance approaches available in the literature. Given the practical nature of the project, SCADA and alarm logs of existing windfarms are used. Results are validated using maintenance logs and verifying the concordance between the predictions and the available information. A key aspect of this investigation is the thorough analysis of real data from a heterogeneous sample of data. The dataset includes four different turbine brands, from seven different windfarms, located in different nations and climates (Spain, United Kingdom and Poland).