摘要
针对水轮机调速系统具有结构复杂、非线性、参数时变等特点,通过力矩流量综合特性建立水轮机神经网络模型,并采用带限幅非线性的电液随动系统模型、简化压力引水系统模型和简化发电机与负载模型,构建水轮机调速系统非线性模型。在此基础上,引用一种无超参数的全局优化算法,即LIPO算法,以综合时间乘以误差绝对值积分(ITAE)指标为目标函数,对调速系统比例—积分—微分(PID)参数进行优化。通过仿真实验对比调速系统在粒子群优化(PSO)算法、遗传算法(GA)和LIPO算法优化参数后的控制性能,结果表明LIPO算法能有效应用于水轮机调速系统PID参数优化,并获得较佳的优化效果。
In view of the complex structure,non-linear and time-varying parameters of hydraulic turbine governing system,neural network model of hydraulic turbine is established based on the comprehensive characteristics of torque and flow,and the nonlinear model of hydraulic turbine governing system is constructed by adopting the electro-hydraulic servo system model with limiting nonlinearity,the simplified pressure diversion system model and the simplified generator and load models.On this basis,a global optimization algorithm,LIPO algorithm,is used to optimize the proportional-integral-differential(PID)parameters of the speed control system with index time times the integral of absolute error(ITAE)index as the objective function.Through the simulation experiment,the control performance of the speed control system after the parameters of particle swarm optimization(PSO),genetic algorithm(GA)and LIPO algorithm are compared.The results show that the LIPO algorithm can be effectively applied to the PID parameter optimization of hydraulic turbine speed control system,and better optimization effect is obtained.
作者
周克良
张自建
邓飞翔
ZHOU Keliang;ZHANG Zijian;DENG Feixiang(School of Electrical Engineering and Automation,Jiangxi University of Science and Technology,Ganzhou 341000,China)
出处
《传感器与微系统》
CSCD
北大核心
2022年第5期95-98,共4页
Transducer and Microsystem Technologies
基金
国家自然科学基金资助项目(61363011)
江西省自然科学基金资助项目(20151BAB207024)。
关键词
水轮机调速系统
非线性模型
PID参数优化
LIPO算法
hydraulic turbine speed governing system
nonlinear models
PID parameter optimization
LIPO algorithm
作者简介
周克良(1963-),男,硕士,教授,硕士研究生导师,研究领域为过程控制与网络集成自动化,智能仪器;通讯作者:张自建(1997-),男,硕士研究生,研究方向为智能控制理论。
