This thesis investigates the use of blade-pitch control and real-time wind measurements to reduce the structural loads on the rotors and blades of wind turbines. The first part of the thesis studies the main similarities between the various classes of current blade-pitch control strategies, which have to date remained overlooked by mainstream literature. It also investigates the feasibility of an estimator design that extracts the turbine tower motion signal from the blade load measurements. In turn, the second part of the thesis proposes a novel model predictive control layer in the control architecture that enables an existing controller to incorporate the upcoming wind information and constraint-handling features. This thesis provides essential clarifications of and systematic design guidelines for these topics, which can benefit the design of wind turbines and, it is hoped, inspire the development of more innovative mechanical load-reduction solutions in the field of windenergy.

Dr Wai Hou (Alan) Lio was born in Macau in 1989. He received his M.Eng inElectrical and Electronic Engineering from Imperial College London in 2012, and hisPh.D. in Automatic Control and Systems Engineering from the University of Sheeldin 2017. He is currently with Department of Wind Energy at Technical University ofDenmark. His main research interests include model predictive control, mathematicaloptimisation and state estimation, with applications in wind energy conversion systemsand wind farms.

Introduction.- Background of Wind Turbine Blade-Pitch Load Reduction Control.- Review of the Related Work.- Performance Similarities between Individual Pitch Control Strategies.- Estimation and Control Design for Tower Motions.- Feed-Forward Model Predictive Control Design based upon a Feedback Controller.- Feed-Forward Model Predictive Control Layer on Wind Turbines.- Conclusions and Future Work.