Fixed speed wind generator model parameter estimation using improved particle swarm optimization and system frequency disturbances

2011 ◽  
Author(s):  
F. Gonzalez-Longatt ◽  
P. Regulski ◽  
P. Wall ◽  
W. Terzija
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Model Parameter ◽  
Improved Particle Swarm Optimization ◽  
Model Parameter Estimation ◽  
Wind Generator ◽  
Generator Model ◽  
System Frequency

A Improved Particle Swarm Optimization and its Application in the Parameter Estimation

2013 ◽  
Vol 373-375 ◽  
pp. 1150-1154
Author(s):  
Tie Bin Wu ◽  
Yun Cheng ◽  
Zhi Kun Hu ◽  
Tao Yun Zhou ◽  
Yun Lian Liu
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Local Search ◽  
Particle Swarm ◽  
Evolutionary Process ◽  
Premature Convergence ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Result Show ◽  
Evolution Stage

For the issues of inferior local search ability and premature convergence in the later evolution stage of the traditional particle swarm optimization, an improved particle swarm optimization is proposed and applied to the parameter estimation. Firstly, in the evolutionary process of particle swarm optimization, the particles which have crossed the border are buffered according to the speed. Then, each particle is performed mutation in different probability according to the evolutionary generations, which can keep the diversity of the particle swarm and avoid the premature convergence effectively. Thirdly, a crossover operation is conducted between the current best particle and the particle which is selected from the particle swarm with a certain probability, which can lead particles gradually approaching to the extreme point and hence, the local search ability of the algorithm will be improved. The advanced particle swarm optimization is applied to the parameter estimation of the kinetic model in the Hg oxidation process and the application result show the effectiveness of the suggested algorithm.

Parameter Estimation of the MISO Nonlinear System Based on Improved Particle Swarm Optimization

2011 ◽  
Vol 130-134 ◽  
pp. 2563-2567 ◽  
Author(s):  
Huai Ke Fan ◽  
Wei Xing Lin
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Nonlinear System ◽  
Parameter Space ◽  
Estimation Method ◽  
Particle Swarm ◽  
Nonlinear Function ◽  
Hammerstein Model ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

Nonlinear system identification is a main topic of modern identification. This paper presents a new parameter estimation method of MISO (multiple inputs, single output) Hammerstein model by using improved particle swarm optimization (IPSO). The basic idea of the method is that the model identification problem is converted into optimization of nonlinear function over parameter space. And the swarm intelligence method is used to search the parameter space concurrently and efficiently in order to find the optimal estimation of the model parameter. The basic algorithms of IPSO and the parameter control are discussed. Simulation results demonstrate effectiveness of the suggested method. The advantages of IPSO are easy to implement, few parameters to adjust, small population size, quick convergence ability and so on. Especially in high noise disturbance condition, the results of IPSO are also satisfactory.

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