## 1 Introduction  
Operation and maintenance activities can reach up to 25% of the overall costs of a wind farm project [1] and this percentage can reach 35% for offshore installations [2]. This in general motivates the widespread interest in wind turbine condition monitoring [3, 4], because the ability to adequately plan interventions for wind turbines is fundamental for diminishing the producible energy losses. In [5], it is reported that for a typical 2 MW wind turbine the cost of the generator is in the order of 10% of the total component cost and that generator failure is among the most impacting factors as regards the number of producible hours lost (around 200). The electric generator is estimated to be among the top three contributors to the failure rates and the downtime of wind turbines, according to [6, 7]. The above matters of fact motivate the importance of developing reliable methodologies for early detection of wind turbine generator damage and for evaluation of their health status. Faults related to wind turbine generators are particularly difficult to diagnose in real-world applications because they evolve quickly in an uncontrolled environment with variable operation conditions. This consideration includes mechanical damage to rotating elements, for example generator bearings, and of course includes electric damage to generator components. Furthermore, in wind energy practice, the most employed information source is constituted by supervisory control and data acquisition (SCADA) data, which have a sampling time of minutes (typically ten). The main drawback is therefore that this time scale is definitely non-optimal, in particular for the diagnosis of electrical faults. In order to acquire electrical measurements with the appropriate sampling frequency (in the order of kHz, as is done for example in [8; 9]), it is at present unavoidable to intervene on site and it is inconceivable to do this on a condition basis. Consequently, the standard is that the inspections of wind turbine generators are periodic and therefore poorly related to the incipience of possible faults. On the grounds of the above considerations, it would be appreciable to develop SCADA data analysis methodologies that could be helpful to evaluate the health status of wind turbine generators. This is exactly the objective of the present study, which is organized as a real-world test-case discussion. The innovative aspects of the present study can be appreciated in light of a brief discussion of the literature about wind turbine generator fault diagnosis, from which it arises that most studies deal with the use of SCADA data regarding mechanical faults. As often happens when SCADA data are employed for this aim [10; 11; 12; 13; 14], the analysis of sub-component temperatures is useful. In [15; 16; 17], the targets are the stator winding temperature, the generator bearing temperature, and the generator slip ring temperatures. In [18], a test case of generator damage (rotor winding failure) is analyzed: the diagnosis is based on a dynamic model sensor method representing the relationship between the generator temperature, wind speed, and ambient temperature. Two inspiring studies for the purposes of the present work are [19; 20]. In [19], a series of phenomena possibly related to wind turbine generator damage are listed and these can be individuated through SCADA data analysis. In addition to anomalous heating, these are miscorrelation between generator speed and produced power, or reactive power, and anomalies in the shaft torque. In [20], it is shown that real-world generator damage can be diagnosed by analyzing the Mahalanobis distance and the correlation matrix of a set of features. As discussed above, the present study aims at contributing to the topic of SCADA data analysis methods for wind turbine generator fault diagnosis.