###### Abstract  
A hybrid health monitoring system for wind turbine generators is introduced. The novelty of this research consists in approaching a 115-wind turbine fleet by using the fusion of multiple sources of information. Analog SCADA data is analyzed through an autoencoder which allows to identify anomalous patterns within the input variables. Alarm logs are processed and merged to the anomaly detection output, creating a reliable health estimator of generator conditions. The proposed methodology has been tested on a fleet of 115 wind turbines from four different manufacturers located in various locations around Europe. The solution has been compared with other existing data modeling techniques offering impressive results on the fleet. An accuracy of 82% and a Kappa of 56% were obtained. The detailed methodology is presented using one of the available windfarms, composed of 13 onshore wind turbines rated 2 MW power. The rigorous evaluation of the results, the utilization of real data and the heterogeneity of the dataset prove the validity of the system and its applicability in an online operating scenario. alarms; anomaly detection; autoencoder; fault detection; SCADA data; generator; predictive maintenance; wind turbines; renewable energy +
Footnote †: journal: Applied Physics Letters  
_Article_  
Wind Energy is one of the main enablers of the ongoing renewable energy revolution. It was reported by WindEurope that in 2016, wind energy production overtook coal as the second largest form of power capacity in Europe, right behind natural gas. The strong increasing trend suggests that it is just a matter of time for wind energy to take the lead [1]. Many challenges are yet to be solved to increase wind energy profitability, and operation and maintenance (O&M) in particular has to be improved. It was reported that unexpected breakdowns typically cause 10-15% of production losses, with extreme peaks of 30% [2]. These losses cripple the profit of energy companies, thus it is not surprising to find optimization of O&M through big data, cloud solutions and innovative technologies as one of the top priorities of the industry [3]. Historically, maintenance has been performed via a reactive approach, based on preventive inspections and corrective interventions once failures were acknowledged. New approaches providing predictive maintenance solutions have emerged both in the academia and the industrial scene. Turbines are commonly equipped with a Supervisory Control and Data Acquisition (SCADA) system, which was initially installed to monitor and operate the system, but lately has been utilized to assess and predict the health status of the turbines as well. SCADA data is recorded by a network of sensors located in the main components of the turbine, the typical sampling frequency is 10 min, making it relatively cheap to collect, transmit and store in a database. All wind fleet operators collect data on their centralized control. SCADA data is collected and stored on Structured Query Language (SQL) databases from SCADA providers or OsiSoft PI system. Early fault detection can be achieved, as shown by [PERSON] and [PERSON], by building a model that captures normal operation of the system and by comparing the difference between predicted and measured values of a key variable, to detect anomalies [4]. This approach does not fully take advantage of the high dimensionality of the SCADA dataset and focuses only on the behavior of a single key variable, while component failures are typically complex and can manifest themselves in different failure modes. The literature is rich with examples based on power curve modeling of wind turbines [5; 6; 7; 8]. This approach is based on tracking the relation between wind speed and output power, the function that describes the relation between these two variables can be inferred from operational data and compared to the one provided by the manufacturer, and significant deviations from the theoretical power curve can be hints of problems in the turbine.