A novel Economic Nonlinear Model Predictive Controller for power maximisation on wind turbines

被引:5
作者
Pustina, L. [1 ]
Biral, F. [2 ]
Serafini, J. [1 ]
机构
[1] Roma Tre Univ, Dept Engn, Rome, Italy
[2] Univ Trento, Dept Mech & Struct Engn, Trento, Italy
关键词
Wind turbine; Control; Power maximisation; Non-linear Model Predictive Control; Non-linear Reduced Order Model; NUMERICAL-METHODS; SPEED;
D O I
10.1016/j.rser.2022.112964
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Reducing the Levelized Cost of Energy (LCoE) is one of the main objectives of the wind turbine industry. There are several ways to achieve this goal: reducing construction and installation costs, reducing Operat-ing&Maintenance costs, or increasing the power output. In this work, an Economic Nonlinear Model Predictive Control strategy is developed to maximise the power production of wind turbines. A novel three-states, non-linear Reduced Order Model is developed to predict aerodynamic power, rotor thrust and generator temperature with suitable accuracy. The control action is obtained from a constrained optimisation problem that uses the developed model, where the objective is the maximisation of the integral of the aerodynamic power. A set of constraints (including a bound on the generator temperature and the rotor thrust) are imposed. First, the turbine model is validated against high-fidelity simulations, then the controller performance and robustness are assessed in the entire wind range of operation, obtaining a significant increase of average power. Apart from the assessment of the controller performance in OpenFAST, the controller robustness is verified, introducing errors in the estimation of incoming wind, up to the case of a complete lack of information. The controller (freely downloadable from a dedicated repository) is effective in all the operating regions without the need for logical switches. Moreover, thanks to the optimised numerical solver adopted, it can be applied to actual wind turbines (which require real-time algorithmic performance).
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页数:16
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