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An Approach for Self Evolving Neural Network Based Algorithm for Fault Prognosis in Wind Turbine

Pramod Bangalore (Institutionen för energi och miljö, Elteknik ; Svenskt VindkraftsTekniskt Centrum (SWPTC)) ; Lina Bertling (Institutionen för energi och miljö, Elteknik)
IEEE Grenoble Conference PowerTech, POWERTECH 2013; Grenoble; France p. (article no 6652218). (2013)
[Konferensbidrag, refereegranskat]

In recent years Supervisory Control and Data Acquisition (SCADA) system has been used to monitor the condition of wind turbine components. SCADA being an integral part of wind turbines comes at no extra cost and measures an array of signals. This paper proposes to use artificial neural networks (ANN) algorithm for analysis of SCADA data for condition monitoring of components. The first step to build an ANN model is to create the training data set. Here an automated process to decide the training data set has been presented. The approach reduces the number of samples in the training data set compared to the conventional method of hand picking the data set. Further the approach describes how the ANN model could be kept in tune with the changes in the operating conditions of the wind turbine by updating the ANN model. The fault prognosis obtained from the model can be used to optimize the maintenance scheduling activity.

Nyckelord: Artificial neural networks, condition monitoring, predictive maintenance, SCADA system, electricity generation.

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Denna post skapades 2013-09-10. Senast ändrad 2015-11-17.
CPL Pubid: 182980


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Institutioner (Chalmers)

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


Elektroteknik och elektronik

Chalmers infrastruktur

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