Floating offshore wind turbine nonlinear model predictive control optimisation method
Javier López-Queija, J. Jugo, Ander Tena, Eider Robles, Eneko Sotomayor
Abstract
This paper presents a novel control parameter optimisation methodology for nonlinear model predictive control for floating offshore wind turbine operation, computing optimisation weights as environment conditions dependent variables. The main objective is to reduce the required time to define the optimal control parameters for the nonlinear control strategy, using an automated approach. To achieve this, an optimisation methodology based on extreme operational gust conditions is applied by employing a Random Walk-type Monte Carlo procedure. The primary aim is to introduce an advanced control design approach that addresses concerns related to the efficient power generation and longevity of floating systems, particularly considering the growing scale of wind turbines and the dynamic behaviour of floating platforms, which increase the system overall costs. The resulting optimised controller is also evaluated against state-of-the-art feedback-based control strategies in different operational environmental conditions. • Random walk optimisation method. • Nonlinear model predictive control for floating wind turbines. • Feedback-based control strategies. • Nonlinear model predictive control weight optimisation procedure. • Fatigue life extension.