Automatic weight determination in nonlinear model predictive control of wind turbines using swarm optimization technique

The Science of Making Torque from Wind

Tofighi, Elham^1 ; Mahdizadeh, Amin^2

School of Electrical Engineering and Computer Science, University of Newcastle, NSW

2308, Australia^1

Department of Electrical and Electronic Engineering, University of Melbourne, VIC

3010, Australia^2

关键词: Intuitive understanding;    Nonlinear model predictive control;    Optimal solutions;    Swarm optimization;    Two-level optimization;    Weight determination;    Weighting coefficient;    Wind turbine control;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/753/5/052033/pdf
DOI  :  10.1088/1742-6596/753/5/052033

来源: IOP

【 摘 要 】

This paper addresses the problem of automatic tuning of weighting coefficients for the nonlinear model predictive control (NMPC) of wind turbines. The choice of weighting coefficients in NMPC is critical due to their explicit impact on efficiency of the wind turbine control. Classically, these weights are selected based on intuitive understanding of the system dynamics and control objectives. The empirical methods, however, may not yield optimal solutions especially when the number of parameters to be tuned and the nonlinearity of the system increase. In this paper, the problem of determining weighting coefficients for the cost function of the NMPC controller is formulated as a two-level optimization process in which the upper- level PSO-based optimization computes the weighting coefficients for the lower-level NMPC controller which generates control signals for the wind turbine. The proposed method is implemented to tune the weighting coefficients of a NMPC controller which drives the NREL 5-MW wind turbine. The results are compared with similar simulations for a manually tuned NMPC controller. Comparison verify the improved performance of the controller for weights computed with the PSO-based technique.