A schooling on wind energy in Trinity

16 Nov 2010

An EU-funded project led by Trinity College Dublin is set to focus on the applications of a wide range of green energy sources.

The Winter School, a €3.2m EU FP7-funded (the seventh EU framework programme for the funding of research and technological development in Europe) project will examine the application of signal processing, wireless sensor networking and system identification principles for the efficient and reliable operation of next-generation wind turbines.

Next-generation turbines

The Winter School aims to monitor the health of next-generation turbines as they grow larger in size and produce more power to ensure security of power supply.

Ten senior scientists from the field of wind energy (drawn from nine European countries) will be joined by more than 30 international researchers from both industry and academia for the event during the project this 15-19 November.

Lecturers appearing at the event are:

–       Dr T Barszcz, director, Energo Control & AGH University of Science and Technology, Poland

Aspects of Wind Turbine Monitoring, Examples on Industrial Applications

–       Prof B Basu, School of Engineering, TCD, Ireland

Output Only and Online System Identification and Control

–       Mr P Blount, Dumore Wind Power Ltd., Ireland

Condition Monitoring from a Wind Farm Developer‘s Perspective

–       Prof S Fassios, University of Patras, Greece

Statistical Time Series for System Identification

–       Dr D Mandic, Imperial College London, UK

Signal Processing and Machine Learning

–       Dr C McGoldrick, School of Computer Science, TCD, Ireland

Wireless Sensor Network (WSN), WSN Demo/Tutorial

–       Mr T Mercer, Garrad Hassan, UK

Wind Turbine Controller Design and Implementation

–       Dr C Meskell, School of Engineering, TCD, Ireland

System Identification for Aeroelastic Systems

–       Prof WJ Staszewski, Sheffield University, UK

Introduction to Condition and Health Monitoring

–       Prof K Worden, Sheffield University, UK

Nonlinear System Identification