基于自适应权重粒子群算法的PSS4B-W参数优化研究

Research on parameter optimization of PSS4B-W based on SWPSO

介绍了一种智能优化算法-自适应权重粒子群算法(SWPSO)应用到电力系统稳定器PSS4B-W的参数整定过程中而建立的PSS4B-W参数优化模型。通过对权重系数的动态调整,从而获得更好的全局搜索能力。根据单机无穷大系统模型,在计算励磁系统无补偿相频特性基础上,应用该优化模型进行PSS4B-W相位补偿计算。仿真结果表明经自适应权重粒子群算法优化的PSS4B-W能够较好地抑制低频振荡,提高系统的稳定性。

An intelligent optimization algorithm called self-adaptive weightiness particle swarm optimization （ SWPSO） is applied to obtain optimal parameters of power system stabilizer PSS 4B-W, and the parameter optimization model for PSS4B-W is established. By adjusting weightiness dynamically, better global search ability is achieved. According to the single machine infinite system model, on the basis of calculating phase frequency characteristic without compensation of excitation system, the parameter optimization model is used to calculate phase compensation of PSS4B-W. The simulation results show that optimized PSS4B-W can suppress low frequency oscillation well and increase power system stability.