基于混合粒子群优化算法的PSS参数优化

将一种新的进化算法—粒子群优化算法(PSO)应用到电力系统稳定器(PSS)参数优化当中,文中使用引入交叉操作的混合粒子群优化算法(HPSO),可以获得更好的全局搜索能力和收敛速度。先以低频振荡范围内(0.1~2Hz)PSS产生的附加阻尼转矩ΔTe与Δω尽可能同相位为目标优化PSS超前-滞后环节参数;再以小扰动时发电机功率和角速度振荡最小为目标整定PSS放大倍数。优化结果表明,HPSO算法可以有效地解决PSS参数优化问题。

Parameters optimization of power system stabilizer using hybrid particle swarm optimization algorithm

A new proposed evolutionary computation called Particle Swarm Optimization (PSO) is applied to obtain optimal parameters of power system stabilizer(PSS).In this paper, a hybrid PSO algorithm with breeding is used,and this revised version is proved to be more probable to find a global optimal solution and to achieve faster convergence. First,the objective function to minimize the angular phase difference between additional damping torque (T_e)resulting from PSS and additional electric angular speed () in the range of low-frequency oscillations (0.1~2Hz) is applied to obtain optimal parameters of lead-lag component of PSS,and then the objective function to minimize the oscillation of electric power and electric angular speed resulting from small disturbances is employed to set the gain of PSS. The proposed methodology has been implemented on a test power system and is proved to be efficient to solve the problem of parameters optimization of PSS in the end.