Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Self Evolving Neural Network Based Algorithm for Fault Prognosis in Wind Turbines: A Case Study
Chalmers.
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, Published 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
2014.
Keyword [en]
Wind power, Asset Management, Reliability assessment, Artificial Neural Networks
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-149739DOI: 10.1109/PMAPS.2014.6960603ISI: 000358734100026Scopus ID: 2-s2.0-84915748406OAI: oai:DiVA.org:kth-149739DiVA: diva2:740956
Conference
International Conference on Probabilistic Methods Applied to Power Systems (PMAPS),Durham, 7 Jul - 10 Jul 2014
Funder
StandUpThe Wenner-Gren Foundation
Note

QC 20140901

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

Open Access in DiVA

2014-PMAPS-WindAM(326 kB)285 downloads
File information
File name FULLTEXT01.pdfFile size 326 kBChecksum SHA-512
db32fdefc6ec231a2889a7c83c6205f4a4860c61beceda490baa83715c3a36b61c6609507a2f2cfb790eff34f88365190d47863fa7c95a9cf0f206d9a83723ff
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopusIEEE ExplorerConference website

Authority records BETA

Bertling Tjernberg, Lina

Search in DiVA

By author/editor
Bertling Tjernberg, Lina
By organisation
Electromagnetic Engineering
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 285 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 250 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf