自适应混沌粒子群算法在PSS设计中的应用

该文采用一种改进的粒子群算法PSO———自适应混沌粒子群算法ACPSO,对多机电力系统稳定器参数进行优化设计,以抑制系统低频振荡。该算法通过混沌初始化粒子群,在迭代计算过程中根据粒子的适应值自适应地调整算法惯性系数,从而可以获得更好的全局搜索能力和收敛速度。选取系统机电振荡模式最小阻尼比最大化为目标函数,将PSS参数优化转换为带不等式约束的非线性优化问题。以3机9节点系统为例,特征值和非线性仿真结果表明,运用该方法设计的PSS能够有效地抑制外界扰动引起的低频振荡。

Design of Power System Stabilizers Using Adaptive Chaos Particle Swarm Optimization Algorithm

In order to damp the low frequency oscillation in power system,an improved particle swarm optimization called adaptive chaos particle swarm optimization(ACPSO) is applied to optimize the parameters of the multi-machine power system stabilizers(PSS).According to the proposed ACPSO algorithm,the capability of global search and convergence rate are improved by using the chaos motion to initialize the swarm,and each particle adjust its inertia weight factor adaptively in accordance with its fitness value in the process of the iterative calculation.The maximized minimum damping among all electromechanical oscillation modes is taken as the objective function,and then the optimization of parameters of the PSS is converted to the nonlinear optimization problem with the inequality constraints.The proposed method is tested on 3 machines and 9 buses system,and the results of both the eigenvalue and nonlinear simulation show that the PSS optimized by proposed ACPSO algorithm can effectively damp the low frequency oscillation caused by external disturbance.