Unsupervised Machine Learning for Anomaly Detection in Wind Turbine Converters
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URL
Journal Title
Journal ISSN
Volume Title
Sähkötekniikan korkeakoulu |
Master's thesis
Authors
Date
2019-06-17
Department
Major/Subject
Electrical Power and Energy Engineering
Mcode
ELEC3024
Degree programme
AEE - Master’s Programme in Automation and Electrical Engineering (TS2013)
Language
en
Pages
56 + 3
Series
Description
Supervisor
Hinkkanen, MarkoThesis advisor
Pirttioja, TeppoKeywords
anomaly detection, unsupervised machine learning, data analysis, wind turbine converter