CPL - Chalmers Publication Library
| Utbildning | Forskning | Styrkeområden | Om Chalmers | In English In English Ej inloggad.

Self Evolving Neural Network Based Algorithm for Fault Prognosis in Wind Turbines : A Case Study

Pramod Bangalore (Institutionen för energi och miljö, Elteknik) ; Lina Bertling Tjernberg (Institutionen för energi och miljö, Elteknik)
2014 International Conference on Probabilistic Methods Applied to Power Systems (Pmaps) (2014)
[Konferensbidrag, refereegranskat]

Asset management of wind turbines has gained increased importance in recent years. High maintenance cost and longer downtimes of wind turbines have led to research in methods to optimize maintenance activities. Condition monitoring systems have proven to be a useful tool towards aiding maintenance management of wind turbines. Methods using Supervisory Control and Data Acquisition (SCADA) system along with artificial intelligence (AI) methods have been developed to monitor the condition of wind turbine components. Various researchers have presented different artificial neural network (ANN) based models for condition monitoring of components in a wind turbine. This paper presents an application of the approach to decide and update the training data set needed to create an accurate ANN model. A case study with SCADA data from a real wind turbine has been presented. The results show that due to a major maintenance activity, like replacement of component, the ANN model has to be re-trained. The results show that application of the proposed approach makes it possible to update and re-train the ANN model.

Nyckelord: Artificial neural networks (ANN), condition monitoring (CMS), SCADA system, electricity generation, maintenance, system, Energy & Fuels, Engineering



Denna post skapades 2015-09-04.
CPL Pubid: 221937

 

Institutioner (Chalmers)

Institutionen för energi och miljö, Elteknik

Ämnesområden

Elektroteknik och elektronik

Chalmers infrastruktur

Relaterade publikationer

Denna publikation ingår i:


Load and risk based maintenance management of wind turbines