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Anomaly Detection and Performance Analysis in Wind Turbines through Neural Networks
KTH, School of Electrical Engineering (EES), Electric power and energy systems.ORCID iD: 0000-0002-0396-3326
KTH, School of Electrical Engineering (EES), Electromagnetic Engineering.
Comillas Pontifical University.
2015 (English)Conference paper, Presentation (Other academic)
Place, publisher, year, edition, pages
College Park, Maryland, USA, 2015.
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering; Energy Technology
Identifiers
URN: urn:nbn:se:kth:diva-191114OAI: oai:DiVA.org:kth-191114DiVA: diva2:954863
Conference
International Workshop on Life-Cycle Costing of Offshore Wind Turbines and Farms
Note

QC 20160923

Available from: 2016-08-23 Created: 2016-08-23 Last updated: 2016-09-23Bibliographically approved

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ReferencesLink to record
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