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An artificial neural network based condition monitoring method for wind turbines, with application to the monitoring of the gearbox

Pramod Bangalore (Institutionen för energi och miljö, Elteknik ; Svenskt VindkraftsTekniskt Centrum (SWPTC)) ; Michael Patriksson (Institutionen för matematiska vetenskaper ; Svenskt VindkraftsTekniskt Centrum (SWPTC)) ; Simon Letzgus (Institutionen för energi och miljö, Elteknik) ; Daniel Karlsson (Institutionen för energi och miljö, Elteknik)
Wind Energy (1095-4244). (2017)
[Artikel, refereegranskad vetenskaplig]

Major failures in wind turbines are expensive to repair and cause loss of revenue due to long down times. Condition based maintenance, which provides a possibility to reduce maintenance cost, has been made possible due to the successful application of various condition monitoring systems (CMS) in wind turbines. New methods to improve the CMS are continuously being developed. Monitoring based on data stored in the supervisory control and data acquisition (SCADA) system in wind turbines has received attention recently. Artificial neural networks (ANN) have proved to be a powerful tool for SCADA based condition monitoring applications. This paper first gives an overview of the most important publications which discuss the application of ANN for condition monitoring in wind turbines. The knowledge from these publications is utilized and developed further with a focus on two areas: the data pre-processing, and the data post-processing. Methods for filtering of data are presented which ensure that the ANN models are trained on the data representing the true normal operating conditions of the wind turbine. A method to overcome the errors from the ANN models due to discontinuity in SCADA data is presented. Furthermore, a method utilizing the Mahalanobis distance is presented, which improves the anomaly detection by considering the correlation between ANN model errors and the operating condition. Finally, the proposed method is applied to case studies with failures in wind turbine gearboxes. The results of the application illustrate the advantages and the limitations of the proposed method.

Nyckelord: Artificial neural network (ANN); condition monitoring system (CMS); Mahalanobis distance; wind energy; supervisory control and data acquisition (SCADA)

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Denna post skapades 2017-02-14.
CPL Pubid: 248115


Institutioner (Chalmers)

Institutionen för energi och miljö, Elteknik (2005-2017)
Svenskt VindkraftsTekniskt Centrum (SWPTC)
Institutionen för matematiska vetenskaperInstitutionen för matematiska vetenskaper (GU)



Chalmers infrastruktur