节点文献
大惯量负载下交流励磁电机柔性制动策略及参数优化
Flexible braking strategy and parameter optimization for AC excitation motors with high-inertia loads
【摘要】 交流励磁电机具有变速恒频与稳态功率解耦的优点,在抽水蓄能、飞轮储能等场景应用中具有显著优势,然而上述场景往往要求大惯量负载下交流励磁电机具备安全紧急制动能力,传统机械制动策略难以直接应用,因此,提出大惯量负载下交流励磁电机柔性制动策略及控制参数优化方法。首先,结合交流励磁电机转子结构特点,提出转子侧接直流励磁电源、定子侧接多级电阻的柔性制动策略,推导其制动工况下的等效电路。其次,建立以多级制动电阻、转子励磁电流、电阻切换时转速为变量,以电机性能和制动电阻功率为约束,以最短制动时间为目标的制动参数优化模型,并采用遗传算法对模型进行求解;最后,通过Matlab/Simulink仿真对多级电阻制动效果与影响因素进行分析,并搭建7 kW交流励磁电机实物测试平台进行验证。结果表明,采用多级电阻柔性制动策略能有效缩短制动时间,控制参数优化可以实现系统功率约束下最短制动时间,兼顾制动效果与制动装置经济性。
【Abstract】 AC excitation motors offer advantages such as constant frequency under variable speed and decoupled power in steady-state operation, making them suitable for applications like pumped storage and flywheel energy storage. However, these applications demand rapid emergency braking under high-inertia loads, which traditional mechanical braking strategies fail to meet. This study proposes a flexible braking strategy and parameter optimization method for high-inertia AC excitation motors. First, based on rotor structural characteristics, the strategy connects the rotor to a DC excitation source and the stator to multi-stage resistors, and derives the equivalent braking circuit. Second, a braking parameter optimization model is established, with multi-stage braking resistance, rotor excitation current, and resistor switching speed as variables; motor ratings and resistor power limits serve as constraints; and the shortest braking time is set as the objective. The model is solved using a genetic algorithm. Finally, multi-stage resistance braking results and influencing factors are analyzed via Matlab/Simulink simulations, and a 7 kW AC excitation motor platform is used to verify the simulations. Results show that the proposed multi-stage flexible braking strategy effectively reduces braking time, and optimized parameters achieve minimal braking duration while satisfying system power constraints, balancing braking efficiency and device economy.
【Key words】 AC motors; braking; the shortest braking time; optimization model; genetic algorithms;
- 【文献出处】 重庆大学学报 ,Journal of Chongqing University , 编辑部邮箱 ,2026年05期
- 【分类号】TM34
- 【下载频次】10