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A Performance and Maintenance Evaluation Framework for Wind Turbines
KTH, School of Electrical Engineering (EES).ORCID iD: 0000-0002-0396-3326
KTH. China Electric Power Research Institute.
KTH, School of Electrical Engineering (EES), Electromagnetic Engineering. (KTH, ETK Department)ORCID iD: 0000-0003-4763-9429
Comillas Pontificia Universidad.
2016 (English)Conference paper (Refereed)
Abstract [en]

In this paper, a data driven framework forperformance and maintenance evaluation (PAME) of windturbines (WT) is proposed. To develop the framework, SCADAdata of WTs are adopted and several parameters are carefullyselected to create a normal behavior model. This model which isbased on Neural Networks estimates operation of WT andaberrations are collected as deviations. Afterwards, in order tocapture patterns of deviations, self-organizing map is applied tocluster the deviations. From investigations on deviations andclustering results, a time-discrete finite state space Markov chainis 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 PreventiveMaintenance efficiency assessment. By comparing the indexescalculated for 9 WTs, current performance and maintenancestrategies can be evaluated, and results demonstrate capabilityand effectiveness of the proposed framework.

Place, publisher, year, edition, pages
Beijing, China: Institute of Electrical and Electronics Engineers (IEEE), 2016.
Keyword [en]
Artificial Intelligence, Maintenance, Markov Processes, Performance Evaluation, Wind Power Generation
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
URN: urn:nbn:se:kth:diva-194384OAI: diva2:1039933
International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Oct. 16-20, 2016 Beijing, China

QC 20161026

Available from: 2016-10-25 Created: 2016-10-25 Last updated: 2016-10-27Bibliographically approved

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Mazidi, PeymanDu, MianBertling Tjernberg, Lina
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