Alzawaideh, BaraBaraAlzawaidehTeimourzadeh Baboli, PayamPayamTeimourzadeh BaboliBabazadeh, DavoodDavoodBabazadehHorodyvskyy, SusanneSusanneHorodyvskyyKoprek, IsabelIsabelKoprekLehnhoff, SebastianSebastianLehnhoff2021-08-242021-08-242021-06-28IEEE Madrid PowerTech (PowerTech 2021)http://hdl.handle.net/11420/10158The ultimate goal of a condition monitoring system for wind turbines (WT) is to predict the upcoming failures; this could be achieved using artificial intelligence techniques. In this paper, a model for detecting excessive temperature anomalies in key components of WT i.e. gearbox, generator and transformer is proposed. This model consists of integrated modules continuously interact following the never-ending learning paradigm based on artificial neural networks addressing the challenge of the limited pre-classified data and lacking of the concept to be learned for a system with continuous change of its operating conditions: (i) the Normal Behavior (NB) module estimates the temperature of the WT key components, (ii) the Expected Time To Failure (ETTF) module calculates the deviation between the estimated normal temperature and the real-time measurement data to predict the upcoming failure of WT key components a few hours before occurring a failure, (iii) in the Anomaly Detection (AD) module, the temperature deviation time series signal is divided into normal or abnormal clusters. The proposed methodology has been applied on a real wind farm data in Germany. The results show that the system could correctly detect a large number of WT upcoming failures, this implies the effectiveness and generalization of the proposed model in terms of classification accuracy.enArtificial neural networkAuto-encodercondition-based maintenanceTime serieswind turbineConference Paper10.1109/PowerTech46648.2021.9495087Other