基于粒子群算法的负荷模型参数辨识研究  

Research on Parameter Identification for Robust Load Model Based on Particle Swarm Algorithm

作　　者：刘影[1] 谢驰[2] 张有润[1] 

机构地区：[1]电子科技大学能源科学与工程学院,四川成都611731 [2]四川大学锦城学院,四川成都611731

出　　处：《四川电力技术》2016年第1期1-5,11,共6页Sichuan Electric Power Technology

基　　金：国家自然青年基金(61401075)

摘　　要：针对负荷模型的稳定性直接影响电力系统分析计算的可靠程度问题,提出了将粒子群算法与分散协调控制相结合的负荷模型参数辨识方法。该负荷模型参数辨识方法根据负荷节点的电压变化情况,通过粒子群优化对含负荷控制的目标函数进行校正,使得模型参数在辨识迭代过程中获得全局最优值,达到负荷模型的最佳稳定性,并通过计算机仿真证实所提出方法能够提高负荷模型的稳健性。For the problem that the stability of load model will directly affect the reliability of power system analysis and calcu- lation, a new parameter identification method for load model is proposed by combining the particle swarm algorithm with the decentralized coordinated control. In the model parameter identification method, the voltage change of load nodes will be con- sidered, and the objective function of load control can be corrected by particle swarm optimization, and the global optimal val- ue for the model parameters can be obtained in the iterative process of identification. Therefore, the best stability of the load model can be achieved. Finally, the simulation results show that the proposed method can effectively improve the identification precision and the robustness of load model.

关 键 词：粒子群算法 负荷模型 参数辨识 协调控制 仿真分析 

分 类 号：TM74[电气工程—电力系统及自动化]