一种改进粒子群算法及其在水轮机控制器PID参数优化中的应用

Improved Particle Swarm Optimization Algorithm and Its Application in Hydraulic Turbine Governor PID Parameters Optimization

提出一种改进的粒子群优化算法,除了个体极值和全局极值外,改进算法中还引入了粒子群的平均位置。因此,粒子可以获得更多的信息来调整自身的状态。基于3个基准测试函数的测试结果显示改进粒子群优化算法具有较好的全局收敛性和收敛精度。计算机仿真结果表明:改进粒子群优化算法应用于水轮机控制器PID参数的优化设计可以有效地改善水轮机控制系统过渡过程的动态性能。

An improved particle swarm optimization （PSO） algorithm is presented. Besides the individual best position and the global best position, the swarm average position is introduced in the improved PSO algorithm. Therefore, more information is acquired by particles to adjust their movements. The test results based on three benchmark functions show that the improved PSO algorithm has a good performance on global convergency and convergence precision. The computer simulation results indicate that the application of the improved PSO algorithm in hydraulic turbine governor PID parameters optimization can effectively improve the dynamic performance of hydraulic transients.