Application Of Intelligent Algorithm In PID Parameter Optimization Of Turbine Governor

The efficient operation and safe production of hydropower station are inseparable from the control of hydraulic turbine regulation system.Among them,the tuning effect of the PID parameter of the governor has a great influence on the hydropower equipment.Simulation of PID parameter setting process is helpful to understand the running state of hydraulic turbine under different working conditions.Therefore,PID parameter setting of hydraulic turbine governor and its simulation have great research value.In this paper,the hydraulic turbine regulation system is analyzed,which is divided into five parts: pressure water diversion system,PID governor,electro-hydraulic follow-up system,generator and hydraulic turbine.In this paper,the genetic algorithm is studied by binary coding and real coding.An improved particle swarm optimization(pso)algorithm based on the improved dynamic learning factor is proposed.Will use the binary coded genetic algorithm,the real-coded genetic algorithm and improved particle swarm algorithm is applied to nonlinear PID governor parameters setting of hydroelectric generating set,the hydroelectric generating set speed deviation ITEA index as the fitness function of the improved particle swarm algorithm,combined the actual plant parameters for simulation using matlab/simulink.Simulation results show that the improved particle swarm optimization(pso)algorithm has the advantages of fast adjustment speed and stable adjustment compared with genetic algorithm and elementary particle swarm optimization(pso).The PID control law optimized by improved particle swarm optimization algorithm(pso)has a better regulating effect on turbine frequency and load disturbance.