基于DESAPSO混合算法的MMC-HVDC系统控制参数优化
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摘要
针对传统外环控制器比例积分(PI)参数的选择需要经过长时间的调试且在大扰动下难以实时调节控制可能导致系统持续振荡的问题,提出了一种基于差分进化模拟退火粒子群优化混合算法(DESAPSO)的MMC-HVDC系统控制参数优化方法。基于MMC-HVDC系统的数学模型,在Matlab/Simulink平台上搭建MMC-HVDC系统仿真模型,采用时间绝对误差积分(ITAE)指标构建PI参数优化的目标函数,利用DESAPSO混合算法对PI参数进行优化。通过对比原参数、基于差分进化算法、模拟退火粒子群优化算法与差分进化模拟退火粒子群优化混合算法的优化结果,验证了该方法在MMC-HVDC控制系统参数优化中的有效性与优越性。
The selection of the PI parameters of the traditional outer loop controller requires a long period test,and it is difficult to adjust and control in real time under the large disturbance of the MMC-HVDC system,which may cause the system to continue to oscillate.This paper presents a method to optimize the control parameters of MMC-HVDC system based on the differential evolution simulated annealing particle swarm optimization(DESAPSO)algorithm.According to the mathematical model of MMC-HVDC system,the simulation model of MMC-HVDC system was built on Matlab/Simulink platform.The optimization method took the time absolute error integral(ITAE)as the objective function and the DESAPSO hybrid algorithm was used to optimize the PI parameters.By comparing the original parameters,the optimization results based on the differential evolution algorithm,the simulated annealing particle swarm optimization algorithm and the differential evolution simulated annealing particle swarm optimization algorithm,the effectiveness and superiority of this method for parameter optimization of the MMC-HVDC control system was verified.
The selection of the PI parameters of the traditional outer loop controller requires a long period test,and it is difficult to adjust and control in real time under the large disturbance of the MMC-HVDC system,which may cause the system to continue to oscillate.This paper presents a method to optimize the control parameters of MMC-HVDC system based on the differential evolution simulated annealing particle swarm optimization(DESAPSO)algorithm.According to the mathematical model of MMC-HVDC system,the simulation model of MMC-HVDC system was built on Matlab/Simulink platform.The optimization method took the time absolute error integral(ITAE)as the objective function and the DESAPSO hybrid algorithm was used to optimize the PI parameters.By comparing the original parameters,the optimization results based on the differential evolution algorithm,the simulated annealing particle swarm optimization algorithm and the differential evolution simulated annealing particle swarm optimization algorithm,the effectiveness and superiority of this method for parameter optimization of the MMC-HVDC control system was verified.
引文
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[2]朱显亮,张英敏,李兴源,等.基于柔性直流低频振荡阻尼控制器的优化设计[J].水电能源科学,2016,34(11):196-199.
[3]胡江,魏星.基于自适应粒子群算法的直流输电PI控制器参数优化[J].电网技术,2008,32(S2):71-74.
[4]崔静思,高凯,韩子娇,等.基于遗传算法的VSCHVDC控制系统PI参数优化[J].电网与清洁能源,2017,33(2):26-31.
[5]邢长达,郭家虎,朱成杰,等.MMC控制系统中子模块控制器的设计[J].电力系统保护与控制,2016,44(3):114-121.
[6]薛英林,徐政.基于箝位双子模块的MMC-HVDC起动控制策略[J].电力系统保护与控制,2013,41(11):1-7.
[7]徐峰,李东海,薛亚丽.基于ITAE指标的PID参数整定方法比较研究[J].中国电机工程学报,2003,23(8):206-210.
[8]刘波,王凌,金以慧.差分进化算法研究进展[J].控制与决策,2007,22(7):721-729.