Professor David Infield

Research Professor

Electronic and Electrical Engineering


Personal statement

I joined the University in 2007 as Professor in Renewable Energy Technologies.  Together with colleagues, a considerable wind energy research activity has been built up since then. For five years I was Manager of the Doctoral Training Centre in Wind Energy Systems, delivering four year PhDs with a considerable training element; this was extended into the Centre for Doctoral Training in Wind and Marine Energy Systems for which I played a leading managerial role before retiring and taking on the role of Research Professor.  My teaching was mainly to these Doctoral students although I also supervised Undergraduate and Masters student projects across a range of renewable energy related topics.

My main responsibility outside the University is as Editor in Chief of the IET’s Renewable Power Generation journal.  

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Accounting for environmental conditions in data-driven wind turbine power models
Pandit Ravi, Infield David, Santos Matilde
IEEE Transactions on Sustainable Energy Vol 14, pp. 168-177 (2023)
Research on the derated power data identification method of a wind turbine based on a multi-Gaussian-discrete joint probability model
Ma Yuanchi, Liu Yongqian, Yang Zhiling, Yan Jie, Tao Tao, Infield David
Sensors Vol 22 (2022)
Wind turbine performance degradation monitoring using DPGMM and Mahalanobis distance
Guo Peng, Gan Yu, Infield David
Renewable Energy Vol 200, pp. 1-9 (2022)
Current status and grand challenges for small wind turbine technology
Bianchini Alessandro, Bangga Galih, Baring-Gould Ian, Croce Alessandro, Cruz José Ignacio, Damiani Rick, Erfort Gareth, Simao Ferreira Carlos, Infield David, Nayeri Christian Navid, Pechlivanoglou George, Runacres Mark, Schepers Gerard, Summerville Brent, Wood David, Orrell Alice
Wind Energy Science Vol 7, pp. 2003-2037 (2022)
Sequential data-driven long-term weather forecasting models' performance comparison for improving offshore operation and maintenance operations
Randit Ravi, Astolfi Davide, Tang Anh Minh, Infield David
Energies — Open Access Journal of Energy Research Vol 15 (2022)
SCADA data for wind turbine data-driven condition/performance monitoring : a review on state-of-art, challenges and future trends
Pandit Ravi, Astolfi Davide, Hong Jiarong, Infield David, Santos Matilde
Wind Engineering Vol 47, pp. 422-441 (2022)

More publications

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Research Interests

My research interests are with electricity generation from renewable energy sources, in particular from wind and to a lesser extent photovoltaics (PV), and the integration of these sources into electricity systems large and small. Associated with this central challenge I take an interest in energy storage technology and application, and increasingly demand side management such as available through controlled charging of electric vehicles.  

Specific areas of research related to wind energy that I am presently active in include:

  • condition monitoring for large offshore turbines;
  • drive train analysis with an emphasis on improving reliability;
  • wind resource spatio-temporal modelling; and
  • power system support services such as frequency support from wind turbines.

Professional Activities

IPCC (External organisation)
Offshore Wind Energy
Delft University of Technology (External organisation)
REN21 (External organisation)
WindEurope Summit 2016
European Academy of Wind Energy (External organisation)

More professional activities


WindEurope Digitalisation Project
Carroll, James (Principal Investigator) Infield, David (Co-investigator) Leithead, Bill (Co-investigator)
01-Jan-2022 - 31-Jan-2023
Simulated Wakes Effect Platform for Turbines 2 (SWEPT2)
Infield, David (Principal Investigator)
"The SWEPT2 project aims to develop a sophisticated tool for modelling of wind turbine wakes and wake interactions. It is well known that present wake models are inadequate, especially for application to large offshore wind farms, and have led to wind farm designs with larger than expected wake losses. Improved wake models are essential for improved wind farm designs with improved energy yield. Validation of wake models is critical but difficult to undertake at full scale. By making use of LIDAR and full size turbines, the project aims to collect data on wakes that will provide confidence in the validation process. However LIDAR data is not without its own technical challenges, mainly related to data dropout due at times to inadequate back-scatter from aerosol particles. Strathclyde has experience of LIDAR measurement of wind turbine wakes both onshore and offshore and will apply the methods previously developed to provide high quality data sets to be used for model validation within the consortium. There are different ways in which flow field measurements and CDF calculations can be compared to assess the quality of wake models; the Strathclyde team will apply methods previously developed and shown to be effective to the SWEPT2 validation."
01-Jan-2015 - 30-Jan-2018
Infield, David (Principal Investigator)
01-Jan-2015 - 31-Jan-2018
EPSRC Centre for Doctoral Training in Wind & Marine Energy Systems | Barrera Martin, Oswaldo
Leithead, Bill (Principal Investigator) Infield, David (Co-investigator) Barrera Martin, Oswaldo (Research Co-investigator)
01-Jan-2014 - 28-Jan-2017
EPSRC Centre for Doctoral Training in Wind & Marine Energy Systems
Leithead, Bill (Principal Investigator) Infield, David (Co-investigator)
01-Jan-2014 - 30-Jan-2022
ClimateXchange Energy Storage Literature Review
Infield, David (Principal Investigator)
01-Jan-2014 - 31-Jan-2014

More projects

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Professor David Infield
Research Professor
Electronic and Electrical Engineering

Tel: 548 2373