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基于粒子群算法,提出了适用于PSS参数优化预整定的自适应加速粒子群算法(SAPSO)。对惯性权重和加速因子等参数进行动态自适应调整,并在粒子搜索过程中最容易陷入局部最优的阶段,有条件地引入随机变异环节来控制粒子寻优行为。建立PSS参数预整定仿真模型,利用SAPSO算法,对某网新投PSS设备进行了参数预整定研究。时域仿真和现场试验表明,预整定PSS参数阻尼效果更好,也验证了新投机组PSS参数预整定方法的有效可行。
Based on Particle Swarm Optimization (PSO), an Adaptive Acceleration Particle Swarm Optimization (PSO) algorithm for PSS pre-tuning is proposed. The parameters of inertia weight and accelerating factor are dynamically and adaptively adjusted. In the process of particle searching, it is most likely to fall into the local optimal stage. The random variation is conditionally introduced to control the particle searching behavior. The pre-tuning simulation model of PSS parameters is established. By using SAPSO algorithm, parameter pre-tuning research is performed on the PSS devices newly deployed in a certain network. The time-domain simulation and field tests show that the pre-set PSS parameter damping effect is better and the PSS parameter pre-setting method of the new speculative set is also validated.