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
A performance and maintenance evaluation framework for wind turbines
KTH. (EPE, ETK Department)ORCID iD: 0000-0002-0396-3326
KTH. (EPE, ETK Department)
KTH, School of Electrical Engineering (EES), Electromagnetic Engineering. (EPE, ETK Department)
2016 (English)In: 2016 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2016 - Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2016Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, a data driven framework for performance and maintenance evaluation (PAME) of wind turbines (WT) is proposed. To develop the framework, SCADA data of WTs are adopted and several parameters are carefully selected to create a normal behavior model. This model which is based on Neural Networks estimates operation of WT and aberrations are collected as deviations. Afterwards, in order to capture patterns of deviations, self-organizing map is applied to cluster the deviations. From investigations on deviations and clustering results, a time-discrete finite state space Markov chain is built for mid-term operation and maintenance evaluation. With the purpose of performance and maintenance assessment, two anomaly indexes are defined and mathematically formulated. Moreover, Production Loss Profit is defined for Preventive Maintenance efficiency assessment. By comparing the indexes calculated for 9 WTs, current performance and maintenance strategies can be evaluated, and results demonstrate capability and effectiveness of the proposed framework.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2016.
Keywords [en]
Artificial Intelligence, Maintenance, Markov Processes, Performance Evaluation, Wind Power Generation, Clustering algorithms, Conformal mapping, Electric power generation, Self organizing maps, Wind power, Wind turbines, Current performance, Evaluation framework, Finite state spaces, Maintenance assessment, Maintenance efficiency, Maintenance strategies, Operation and maintenance, Preventive maintenance
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:kth:diva-207457DOI: 10.1109/PMAPS.2016.7763931ISI: 000392327900008Scopus ID: 2-s2.0-85015168830ISBN: 9781509019700 (print)OAI: oai:DiVA.org:kth-207457DiVA, id: diva2:1096913
Conference
2016 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2016, 16 October 2016 through 20 October 2016
Note

QC 20170519

Available from: 2017-05-19 Created: 2017-05-19 Last updated: 2017-05-19Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Mazidi, PeymanDu, Mian

Search in DiVA

By author/editor
Mazidi, PeymanDu, MianBertling Tjernberg, Lina
By organisation
KTHElectromagnetic Engineering
Energy Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 6 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