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Wind Turbine Prognostics and Maintenance Management based on a Hybrid Approach of Neural Networks and Proportional Hazards Model
KTH, School of Electrical Engineering (EES), Electric power and energy systems. Comillas Pontifical University.ORCID iD: 0000-0002-0396-3326
KTH, School of Electrical Engineering (EES), Electromagnetic Engineering.ORCID iD: 0000-0003-4763-9429
Comillas Pontifical University.
2016 (English)In: Journal of Risk and Reliability, ISSN 1748-006X, E-ISSN 1748-0078Article, review/survey (Refereed) Published
Abstract [en]

This paper proposes an approach for stress condition monitoring and maintenance assessment in wind turbines(WT) through large amounts of collected data from supervisory control and data acquisition (SCADA) system. Theobjectives of the proposed approach are: to provide a stress condition model for health monitoring, to assess the WT’smaintenance strategies, and to provide recommendations on current maintenance schemes for future operations ofthe wind farm. At first, several statistical techniques, namely Principal component analysis, Pearson, Spearman andKendall correlations, mutual information, regressional ReliefF and decision trees are used and compared to assessthe data for dimensionality reduction and parameter selection. Next, a normal behavior model is constructed by anartificial neural network which performs condition monitoring analysis. Then, a model based on mathematical form ofProportional hazards model is developed where it represents stress condition of the WT. Finally, those two modelsare jointly employed in order to analyze the overall performance of the WT over the study period. Several cases areanalyzed with a five-year SCADA data and maintenance information is utilized to develop and validate the proposedapproach.

Place, publisher, year, edition, pages
Keyword [en]
Wind Turbine, Condition Monitoring, Prognostics, Maintenance Management, Neural Networks
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering; Planning and Decision Analysis; Industrial Engineering and Management
URN: urn:nbn:se:kth:diva-196889OAI: diva2:1049527

QCR 20161207

Available from: 2016-11-24 Created: 2016-11-24 Last updated: 2016-12-07Bibliographically approved

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