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Personal profile

Personal Statement

I am a Lecturer and a Strathclyde Chancellor’s Fellow in the Department of Electronic & Electrical Engineering. I obtained my PhD in Wind Energy Maintenance Cost and Turbine Reliability from the EPSRC Wind Energy Systems CDT, hosted at the University of Strathclyde. Following my PhD I was awarded an EPSRC Doctoral Prize to research wind turbine component failure and remaining useful life prediction. I continue to carry out research in both the areas of my PhD and Post Doc, as well as the area of novel wind turbine concept development. 
Before my time at the University of Starthclyde I worked for a wind turbine developer in Germany and a wind turbine manufacturer and operator in both Germany and Denmark. My research is driven and made possible by close links to a number of leading wind turbine developers, manufacturers and operators. 

Research Interests

My research focuses on the wind energy area, both onshore and offshore. I have a particular interest in:

- Wind turbine reliability

- O&M cost modelling

- Wind Turbine cost of energy modelling

- The impact of turbine drive train selection on reliability and cost

- Wind turbine component condition monitoring

- Wind turbine component failure prediction

- Wind turbine component remaining useful life prediction based on data driven machine learning approaches and physical modelling approaches

- SCADA and Vibration data analytics

- Novel wind turbine concept development

Fingerprint Dive into the research topics where James Carroll is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

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Wind turbines Engineering & Materials Science
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Permanent magnets Engineering & Materials Science

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Projects 2012 2027

Research Output 2014 2019

Comparison of anomaly detection techniques for wind turbine gearbox SCADA data

Mckinnon, C., Carroll, J., McDonald, A., Koukoura, S. & Soraghan, C., 17 Jun 2019. 1 p.

Research output: Contribution to conferenceAbstract

Open Access
File
Wind turbines
Support vector machines
Turbines
Neural networks

Machine learning in wind turbine O&M

Mckinnon, C., Carroll, J., McDonald, A. & Koukoura, S., 7 Mar 2019.

Research output: Contribution to conferencePoster

Open Access
File
Wind turbines
Learning systems
Wind power
Statistics