Thesis

Applications of machine learning in diagnostics and prognostics of wind turbine high speed generator failure

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Awarding institution
  • University of Strathclyde
Date of award
  • 2021
Thesis identifier
  • T16058
Person Identifier (Local)
  • 201651336
Qualification Level
Qualification Name
Department, School or Faculty
Abstract
  • The cost of wind energy has decreased over the last decade as technology has matured and the industry has benefited greatly from economies of scale. That being said, operations and maintenance still make up a significant proportion of the overall costs andneeds to be reduced over the coming years as sites, particularly offshore, get largerand more remote. One of the key tools to achieve this is through enhancements ofboth SCADA and condition monitoring system analytics, leading to more informedand optimised operational decisions. Specifically examining the wind turbine generator and highspeed assembly, this thesis aims to showcase how machine learning techniques can be utilised to enhance vibration spectral analysis and SCADA analysis for early and more automated fault detection. First this will be performed separately based on features extracted from the vibration spectra and performance data in isolation before a framework will be presented to combine data sources to create a single anomaly detection model for early fault diagnosis. Additionally by further utilising vibration based analysis, machinelearning techniques and a synchronised database of failures, remaining useful life prediction will also be explored for generator bearing faults, a key component when itcomes to increasing wind turbine generator reliability. It will be shown that throughearly diagnosis and accurate prognosis, component replacements can be planned andoptimised before catastrophic failures and large downtimes occur. Moreover, resultsalso indicate that this can have a significant impact on the costs of operation andmaintenance over the lifetime of an offshore development.
Advisor / supervisor
  • McDonald, Alasdair
  • Carroll, James
Resource Type
DOI
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