17:00 - 18:30 Condition-based decision support
This session will describe current and explore application of new methods and sensors for the condition monitoring of wind turbines. Laboratory and field-based technologies will be explored and their operational effectiveness and expected added value quantified.
- Analyse new methods for monitoring turbine health and performance
- Quantify the operational effectiveness of condition-based monitoring
- Explore new sensors for turbine health monitoring
- Quantify the value of prognostics
Lead Session Chair:
Simon Watson, Loughborough University, United Kingdom
Michael Secker (1) F P Christopher J. Crabtree (1) Donatella Zappala (1)
(1) Durham University, Durham, United Kingdom
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Presenter's biographyBiographies are supplied directly by presenters at EWEA 2015 and are published here unedited
Donatella Zappalá earned the MSc Environmental Engineering Degree at Universitá di Perugia, Italy, in 2008. She graduated with the MSc in New and Renewable Energy from Durham University, UK, in 2010 and received the PhD degree from Durham University in 2015 with a thesis on advanced condition monitoring techniques for wind turbines. She is currently Research Associate in the School of Engineering and Computing Sciences at Durham University with research interests including the development of wind turbine electrical and mechanical fault detection algorithms by using the condition monitoring test rig developed at Durham University.