Parameter Optimization Of Excitation Control System Based On Improved Particle Swarm Optimization

With the continuous improvement of the penetration rate of new energy generation and the coverage rate of power electronic equipment in the power system,the security challenges faced by the power system are becoming more and more serious.The excitation control of synchronous generator is of great significance to ensure the safety and stable operation of power system.At present,PID controller is widely used in excitation control system,but the resistance of PID controller to interference is weak,and the traditional PID controller parameter tuning method is not adaptive in complex and nonlinear excitation control system.As one of swarm intelligence optimization algorithms,particle swarm optimization(PSO)has attracted much attention from scholars because of its good robustness,simple tuning parameters,excellent convergence speed and convergence accuracy.Therefore,this thesis studies the parameter optimization of excitation control system based on improved particle swarm optimization algorithm.The main work of this thesis is as follows:The research significance,development history and working principle of the excitation system are analyzed,and the help of the excitation system to improve the stability of the power system is explained from three aspects: static stability,transient stability and dynamic stability.Then the mathematical model of synchronous generator is studied,the basic equation of synchronous generator is built based on Park transformation,and a practical simplified mathematical model of synchronous generator is established according to theoretical basis and practical needs.Finally,combined with the theoretical characteristics and structural composition of the excitation control system,a complete mathematical model of the excitation control system and a simulation model based on Simulink platform are established.Based on the mechanism of particle swarm optimization algorithm,a particle swarm optimization algorithm based on personalized adjustment of inertia weight(PAPSO)is proposed.In PAPSO,the particles in the population are given personalized inertia weights according to the quality of the current optimization position and the experience of the previous optimization,and the backtracking factor is introduced to avoid particles falling into the trap of local optimization.At the same time,the opposition-based learning is applied to optimize the initial population,so as to speed up the convergence speed and improve the convergence accuracy of the algorithm.PAPSO and other three improved particle swarm optimization algorithms are used to optimize 16 kinds of optimization test functions respectively,and the comprehensive ranking is carried out according to the three performance indexes of average value,optimal value and variance.The results show that PAPSO ranks first in the average value,optimal value and variance of 10 optimization test functions,and the theoretical optimal value can be searched for 40 independent optimizations in 7optimization test functions,which indicates that PAPSO has more advantages in comprehensive performance.PAPSO is applied to the parameter optimization of the PID controller of the excitation control system.Compared with other three improved particle swarm optimization algorithms,PAPSO has higher precision in fitness function convergence,and the excitation control system optimized by PAPSO has shorter rise time,lower overshoot and smaller steady-state error.In order to solve the problem that PID controller in excitation control system cannot resist sudden disturbance,the fuzzy control is combined with the excitation control system,and PAPSO is used to optimize the parameters of proportional factor in fuzzy controller.The comparative analysis of load disturbance experiment and synchronous generator disturbance experiment shows that the fuzzy excitation control system optimized by PAPSO has stronger resistance and faster recovery speed to burst disturbance.