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Multi-objective Genetic Algorithm-Based Optimization of DFIG Voltage and Current Control for Wind Energy Systems
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DOI：10.1007/s12555-026-00048-z.png)
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Abstract
En 中文
This paper presents a multi-objective genetic algorithm approach for optimizing voltage and current control in a doubly-fed induction generator wind energy system. A comprehensive MATLAB/Simulink model is developed, and a multi-objective optimization problem is formulated to improve voltage and current regulation under dynamic operating conditions. The proposed method automatically tunes nine gains (Kp,v,Ki,v,Kp,id,Ki,id,Kp,idr,Ki,idr,Kp,iqr,Ki,iqr,Kpg)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ (K_{p,v},\ K_{i,v},\ K_{p,id},\ K_{i,id},\ K_{p,idr},\ K_{i,idr},\ K_{p,iqr},\ K_{i,iqr},\ K_{pg}) $$\end{document} by seamlessly integrating MATLAB code with Simulink, enabling automated parameter assignment, simulation execution, and performance evaluation. This flexible framework is compatible with a wide range of controller structures (e.g., PI), making it adaptable to various control strategies. Simulation results under a turbulent wind profile demonstrate significant improvements in both voltage and current regulation, as well as reductions in overshoot and harmonic distortion for the system. Additionally, the proposed approach provides a set of optimal solutions for trade-off analysis, offering a systematic and effective framework for balancing multiple control objectives.
Keywords:
DFIG
Wind energy
Multi-objective optimization
Genetic algorithm
Controller tuning
Pareto front
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2.9
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170
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6.5K
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