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

Area of Science:

• Electrical Engineering
• Renewable Energy Systems
• Control Theory

Background:

• Wind turbine generators face operational instability during grid-voltage surges.
• Traditional control methods may not adequately address transient voltage disturbances.
• Rotor-side converter dynamics and controller behavior under transients require detailed investigation.

Purpose of the Study:

• To present an advanced control strategy for enhancing wind turbine generator stability during grid-voltage surges.
• To propose a multiobjective optimization framework using an improved nondominated sorting genetic algorithm II (NSGA-II).
• To investigate the dynamic model of the rotor-side converter and proportional-integral-derivative (PID) controllers under voltage transients.

Main Methods:

• Development of a dynamic model for the rotor-side converter.
• Investigation of proportional-integral-derivative (PID) controller dynamics under voltage transients.
• Implementation of an improved nondominated sorting genetic algorithm II (NSGA-II) for multiobjective optimization.

Main Results:

• The improved NSGA-II demonstrated superior robustness compared to traditional NSGA-II, particle swarm optimization, and gray wolf optimization algorithms.
• The proposed strategy effectively suppresses equipment wear during transient conditions.
• Significant minimization of harmonic distortions under transient voltage conditions was achieved.

Conclusions:

• The developed control strategy enhances the operational stability of wind turbine generators during grid-voltage surges.
• The improved NSGA-II based framework offers a robust solution for mitigating transient impacts.
• This advancement contributes to improved grid resilience and operational efficiency in wind power systems.