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Performance Analysis and Anomaly Detection in Wind Turbines based on Neural Networks and Principal Component Analysis
KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
KTH, Superseded Departments, Electrical Systems. KTH, School of Electrical Engineering (EES), Electromagnetic Engineering. KTH, Superseded Departments, Electric Power Systems.ORCID iD: 0000-0003-4763-9429
2017 (English)Conference paper, Abstract (Refereed)
Abstract [en]

This paper proposes an approach for maintenancemanagement of wind turbines based on their life. The proposedapproach uses performance analysis and anomaly detection(PAAD) which can detect anomalies and point out the originof the detected anomalies. This PAAD algorithm utilizes neuralnetwork (NN) technique in order to detect anomalies in theperformance of the wind turbine (system layer), and then appliesprincipal component analysis (PCA) technique to uncover theroot of the detected anomalies (component layer). To validatethe accuracy of the proposed algorithm, SCADA data obtainedfrom online condition monitoring of a wind turbine are utilized.The results demonstrate that the proposed PAAD algorithm hasthe capability of exposing the cause of the anomalies. Reducingtime and cost of maintenance and increasing availability and inreturn profits in form of savings are some of the benefits of theproposed PAAD algorithm.

Place, publisher, year, edition, pages
2017.
Keyword [en]
Wind Turbine, Anomaly Detection, Maintenance, Performance
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-207849OAI: oai:DiVA.org:kth-207849DiVA: diva2:1098848
Conference
12TH WORKSHOP ON INDUSTRIAL SYSTEMS AND ENERGY TECHNOLOGIES (JOSITE2017), MADRID, SPAIN
Note

QC 20170824

Available from: 2017-05-26 Created: 2017-05-26 Last updated: 2017-08-24Bibliographically approved

Open Access in DiVA

PAAD Algorithm(8487 kB)3 downloads
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Type fulltextMimetype application/pdf

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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