Research On Intelligent Optimization Of Synchronous Generator Excitation Control System

In recent years,the problem of power system stability has become more and more prominent.Excitation control plays an active role in ensuring the safe and reliable operation of the power system and improving the power supply quality of the generator.However,the traditional PID control can hardly meet the operation requirements of modern power system.In order to improve the dynamic quality of excitation control system,based on the study of excitation control system characteristics and comprehensive learning particle swarm optimization(CLPSO),this paper proposes an improved CLPSO algorithm(improved CLPSO)based on crossover strategy and adaptive inertial weight strategy to optimize the excitation control system.In this paper,the research status and research trends of excitation control system are analyzed.The related theory of excitation system is systematically studied.The influence of excitation system on the stability of large and small disturbances of power system and its stability criterion are studied.The excitation PID control system is applied.The working principle,parameter setting method and performance evaluation index were studied.According to the characteristics of excitation control system,the mathematical model of synchronous generator excitation control system is studied.This paper focuses on the research of the mathematical model of synchronous generator based on Park equation,and introduces the practical parameters of the motor into the mathematical model of synchronous generator.Then,according to the research needs,the simulation model of nonlinear excitation control system of synchronous generator required in this paper is established.Particle swarm optimization(pso)and its improved algorithm are studied systematically.On the basis of basic particle swarm optimization(pso)research,comprehensive learning pso algorithm is further studied.In order to solve the defects of CLPSO algorithm,cross strategy and adaptive inertia weight strategy are introduced to improve the CLPSO algorithm,and the improved CLPSO algorithm is proposed.The basic PSO algorithm,CLPSO algorithm and improved CLPSO algorithm are tested by 14 basic test functions.The results show that the improved CLPSO algorithm can obtain better solutions among 11 functions,which proves that it has better comprehensive performance.The improved CLPSO algorithm is applied to the design of PID excitation controller,and the simulation experiment is carried out in MATLAB/Simulink environment.Through comparative analysis of parameter optimization experiments,compared with CLPSO algorithm and basic PSO algorithm,the improved CLPSO algorithm has better fitness value,smaller overshoot and faster solving speed.After that,the three algorithms are applied to the excitation control system for simulation experiment Through excitation experiment,load voltage disturbance experiment and system quality parameter disturbance experiment,it is further proved that the improved CLPSO algorithm has stronger ability to improve the system control performance and anti-interference.