Lv Yanling, Zhao Xiang, Mou Zexin
School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin, Heilongjiang Province, China.
State Grid Heilongjiang Electric Power Co., Ltd., Electric power Research Institute, Harbin, Heilongjiang Province, China.
PLoS One. 2025 Jun 23;20(6):e0326077. doi: 10.1371/journal.pone.0326077. eCollection 2025.
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.
本文提出了一种先进的控制策略，以提高风力发电机组在电网电压浪涌期间的运行稳定性。具体而言，通过建立转子侧变流器的动态模型并研究电压暂态下比例积分微分控制器的运行动态，提出了一种基于改进的非支配排序遗传算法II（NSGA-II）的多目标优化框架。使用传统的NSGA-II、多目标粒子群优化算法和多目标灰狼优化算法进行了对比仿真，以验证所提算法。改进的NSGA-II在抑制暂态条件下的设备磨损和最小化谐波失真方面表现出卓越的鲁棒性。这些进展突出了所提框架在增强风电系统电网弹性和运行效率方面的潜力。
