Open AccessComputationally efficient model predictive control of complex wind turbine models
Author(s) -
Evans Martin A.,
Lio Wai Hou
Publication year - 2022
Publication title -
wind energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.743
H-Index - 92
eISSN - 1099-1824
pISSN - 1095-4244
DOI - 10.1002/we.2695
Subject(s) - model predictive control , drivetrain , turbine , control theory (sociology) , aeroelasticity , engineering , control engineering , controller (irrigation) , wind power , process (computing) , torque , computer science , aerodynamics , control (management) , mechanical engineering , agronomy , physics , electrical engineering , artificial intelligence , biology , thermodynamics , aerospace engineering , operating system
As wind turbines are designed with longer blades and towers, it becomes increasingly important to factor structural modes into the design of the controller. In classical turbine controllers, where pitch‐speed, torque‐speed, drivetrain and tower dampers are designed separately, it has for years been commonplace to base that design on a linearisation of the existing high‐fidelity aeroelastic model. Furthermore, any measurement filters that are required at run‐time are included in the control loop shaping process. In contrast, most previous work on model predictive control (MPC) for wind turbines uses simplified models and ignores the need or effect of measurement filters. In this work, we demonstrate a mostly automatic design process that takes a detailed linearised model from an aeroelastic simulation package and adds linear filters and feedback, to produce a model predictive controller with low run‐time computational complexity. The tuning process is substantially simpler than classical control, making it an attractive tool in industrial applications.