摘要
飞轮储能系统中飞轮电机非线性、变参数等特性的存在,导致其充电控制系统转速环PID控制器的参数整定存在一定的困难。将人工蜂群算法应用于飞轮充电控制系统中对PID参数进行优化。提出了基于人工蜂群算法优化PID参数的具体流程,分别采用人工蜂群算法、遗传算法和蚁群算法优化飞轮充电控制系统PID控制器,并通过仿真进行性能对比分析,结果表明基于人工蜂群算法整定的PID控制器的阶跃响应具有最短的上升时间和最少的迭代次数,效果最好,验证了人工蜂群算法优化飞轮充电的优越性。
It is difficult to definite the PID controller parameters in the speed loop of the flywheel charging control system,owing to the nonlinearity and the varying parameters of the flywheel motor.Artificial Bee Colony（ABC） algorithm is introduced to optimize the PID parameters in the control system of flywheel charging.This paper presents the whole process of optimizing the PID parameters by ABC algorithm.Then comparison is made in terms of the effect of optimizing the PID parameters between ABC algorithm,genetic algorithm and ant colony algorithm.Simulation results show that the step response of ABC has the shortest rise time,least iteration and the most steady state accuracy,which verify the superiority of ABC algorithm in optimizing the flywheel charging system.
出处
《华东电力》
北大核心
2011年第9期1500-1504,共5页
East China Electric Power
基金
国家自然科学基金青年项目资助(51007030)~~
关键词
飞轮充电控制系统
人工蜂群算法
PID控制器
参数优化
flywheel charging control system
artificial bee colony algorithm
PID controller
parameters optimization
