In recent years, the classic actuator disc theory has been extended to describe the performance of turbines in bounded flows (such as in wind tunnels or tidal channels) and to explore the potential for different arrangements of turbines to boost their combined power output. In this talk, Paul will briefly discuss his ongoing work to test whether these simple theories have identified the best way to arrange turbines in tidal channels.

Paul is a fourth year PhD student in the Institute for Energy Systems. In his first year, he focused on the social and ecological impacts of wave and tidal current energy generation. Since then, he has been thinking about the best way to arrange turbines in tidal channels. Paul also has a BE in Civil Engineering from the National University of Ireland, Galway, and an MSc in Sustainable Energy Systems from the University of Edinburgh.

 

This presentation discusses a study of the reliability performance (time to failure prediction) of small wind turbines using Bayesian Hierarchical Modelling. Two models are proposed which use warranty record data for 573 operational wind turbines within a commercial fleet and historical wind profiles (for a subset of the turbines) to quantify overall turbine reliability and the reliability of individual components within the turbine. The models are fitted to data within a Bayesian framework, using Markov Chain Monte Carlo, because the flexibility of this approach allows the model structure to capture features that would not be captured by the standard Weibull model for reliability analysis.

 

JenHao Wu received a master degree in electrical and control engineering department at National Chiao Tung University, Hsinchu, Taiwan, in 2009. He is currently working toward the Ph.D. degree at the Institute for Energy System, The University of Edinburgh, UK. His research interests include statistical reliability analysis, renewable energy, and power electronics.