基于混合粒子群的水轮机调速系统参数辨识

Parameter Identification of Hydro Turbine Governing System Based on Hybrid Particle Swarm Optimization Algorithm

水轮机调速系统模型及参数的准确性对电力系统的稳定分析计算至关重要。鉴于传统的水轮机模型过于简化不符合系统实际运行情况,采用非线性的水轮机-引水系统模型,并将混合粒子群优化算法应用于水轮机调速系统的参数辨识中。此算法在粒子群算法的基础上融入了混沌和差分进化思想,在种群初始化时改善初始种群,对陷入早熟的粒子进行改进,提高种群的多样性,同时又能有效地解决局部最优问题。仿真结果表明,混合粒子群算法能快速且准确地辨识出水轮机各环节的参数。

The model and parameters of hydro turbine governing system are essential to the power system stability analysis and calculations. Given the traditional turbine model is too simplified to meet the actual operation condition, this paper adopted a nonlinear turbine-diversion system model,and a hybrid particle swarm optimization algorithm was applied to the parameter identification.Based on the particle swarm optimization algorithm, the hybrid particle swarm optimization algorithm absorbed differential evolution and chaos ideology. It can improve the initial population and the diversity of population. Also it can avoid falling into the local optimum. The results show that the proposed algorithm has high parameter identification precision, it is superior and feasible.