Picture of person typing on laptop with programming code visible on the laptop screen

World class computing and information science research at Strathclyde...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by researchers from the Department of Computer & Information Sciences involved in mathematically structured programming, similarity and metric search, computer security, software systems, combinatronics and digital health.

The Department also includes the iSchool Research Group, which performs leading research into socio-technical phenomena and topics such as information retrieval and information seeking behaviour.

Explore

Nonlinear predictive generalized minimum variance LPV control of wind turbines

Savvidis, Petros and Grimble, Michael and Majecki, Pawel and Pang, Yan (2017) Nonlinear predictive generalized minimum variance LPV control of wind turbines. In: 5th IET International Conference on Renewable Power Generation (RPG) 2016. IET, Stevenage. ISBN 9781785613012

[img]
Preview
Text (Savvidis-etal-RPG-2016-Nonlinear-predictive-generalized-minimum-variance-LPV-control-of-wind-turbines)
Savvidis_etal_RPG_2016_Nonlinear_predictive_generalized_minimum_variance_LPV_control_of_wind_turbines.pdf - Accepted Author Manuscript

Download (290kB) | Preview

Abstract

More advanced control strategies are needed for use with wind turbines, due to increases in size and performance requirements. This applies to both individual wind turbine controls and for the total coordinated controls for wind farms. The most successful advanced control method used in other industries is predictive control, which has the unique ability to handle hard constraints that limit system performance. However, wind turbine control systems are particularly difficult in being very nonlinear and dependent upon the external parameter variations which determine behaviour. Nonlinear controllers are often complicated to implement. The approach proposed here is to use one of the latest predictive control methods which can be used with linear parameter varying (LPV) models. These can approximate the behaviour of nonlinear wind turbines and provide a simpler control structure to implement. The work has demonstrated the feasibility and benefits that may be obtained.