Dynamic Modeling Method for Generator's Excitation System Based on Smart Component Technique

Abstract:

The off-line test method which is generally employed in modeling of generator excitation system has parameter lag and does not fully reflect dynamic characteristics. This article established an online identification and dynamic modeling for the excitation system by smart component technique combined with particle swarm optimization algorithm. It studied the principles of dynamic modeling, taking model parameters corresponding to the minimum objective function as the output result of the identification, also set up implementation steps and flowcharts of the parameter identification based on particle swarm optimization algorithm. An example case was given based on the measured data, and the identification results of the case were taken as the set value of the simulation model. The article compared simulation results to the field test data of 5% step response condition. The comparative results indicated that particle swarm optimization algorithm based on synchronization intelligent component data had the ability to adapt to online identification decently and high identification accuracy.

• Periodical: Applied Mechanics and Materials (Volumes 513-517)
• Pages: 2989-2994
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.513-517.2989
• Online since: February 2014
• Authors: Fu Xing Li*, Jian Zhang
• Keywords: Dynamic Modeling, Excitation System, On-Line Parameter Identification, Particle Swarm Optimization Algorithm

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