Optimization of rotor-side controller parameters in doubly fed induction generators based on an improved NSGA-II

Abstract

Herein, an advanced control strategy to enhance the operational stability of wind turbine generators during grid-voltage surges is presented. In particular, a multiobjective optimization framework based on an improved nondominated sorting genetic algorithm II (NSGA-II) is proposed by establishing a dynamic model of the rotor-side converter and investigating the operational dynamics of proportional–integral–derivative controllers under voltage transients. Comparative simulations using the traditional NSGA-II, a multiobjective particle swarm optimization algorithm, and a multiobjective gray wolf optimization algorithm are conducted to validate the proposed algorithm. The improved NSGA-II exhibits superior robustness in suppressing equipment wear and minimizing harmonic distortions under transient conditions. These advancements highlight the potential of the proposed framework for enhancing grid resilience and operational efficiency in wind power systems.