Optimized economic nonlinear model predictive control for wind turbine: A superior approach for enhanced power and structural load reduction

Abstract

Maximizing power output from wind turbines while minimizing harmful loads is a critical challenge for operators. Previous research explored the use of economic nonlinear model predictive control for wind turbine operation, but several important aspects were not fully addressed. This study advances the field by optimizing the weighting coefficients of the economic nonlinear model predictive control to improve performance. A comprehensive comparison is carried out between this method and conventional proportional–integral control. In addition, the economic nonlinear model predictive control is evaluated under extreme wind gust conditions in accordance with industry standards. Results demonstrate that the method increases power generation, reduces fatigue and structural loads on turbine components, and shows superior performance under both normal and gusty wind conditions. These findings highlight the potential of the optimized economic nonlinear model predictive control as a robust and effective strategy for improving efficiency and reliability in wind turbine operations.