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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KTH, Superseded Departments (pre-2005), Electric Power Systems.ORCID iD: 0000-0003-4763-9429
Comillas Pontifical University.
2016 (English)In: Journal of Risk and Reliability, ISSN 1748-006X, E-ISSN 1748-0078Article in journal, Editorial material (Refereed) Published
Abstract [en]

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

Place, publisher, year, edition, pages
2016.
Keyword [en]
Wind turbine, condition monitoring, prognostics, maintenance management, neural networks
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Industrial Biotechnology
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-219630OAI: oai:DiVA.org:kth-219630DiVA: diva2:1164290
Note

QC 20171215

Available from: 2017-12-11 Created: 2017-12-11 Last updated: 2017-12-15Bibliographically approved

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Authority records BETA

Mazidi, PeymanBertling Tjernberg, Lina

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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  • de-DE
  • en-GB
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Output format
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