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Self Evolving Neural Network Based Algorithm for Fault Prognosis in Wind Turbines: A Case Study
KTH, School of Electrical Engineering (EES), Electromagnetic Engineering.ORCID iD: 0000-0003-4763-9429
2014 (English)In: International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 2014Conference paper (Refereed)
Abstract [en]

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. 

Place, publisher, year, edition, pages
Keyword [en]
Wind power, Asset Management, Reliability assessment, Artificial Neural Networks
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
URN: urn:nbn:se:kth:diva-149739DOI: 10.1109/PMAPS.2014.6960603ISI: 000358734100026ScopusID: 2-s2.0-84915748406OAI: diva2:740956
International Conference on Probabilistic Methods Applied to Power Systems (PMAPS),Durham, 7 Jul - 10 Jul 2014
StandUpThe Wenner-Gren Foundation

QC 20140901

Available from: 2014-08-26 Created: 2014-08-26 Last updated: 2015-09-11Bibliographically approved

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2014-PMAPS-WindAM(326 kB)206 downloads
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Bertling Tjernberg, Lina
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