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Chaotic Ant Swarm Based Parameter Estimation of Induction Motor from Manufacturer Data
| Content Provider | Semantic Scholar |
|---|---|
| Author | Yan-Jie, Li Zhu-Zhi, Jia |
| Copyright Year | 2013 |
| Abstract | A method of parameter estimation of induction motor based on optimization using a chaotic swarm algorithm is presented. The parameter estimation methodology describes a method for estimating the steady-state equivalent circuit parameters from the motor performance characteristic, which is normally available from manufacturer data or from tests. The optimization problem is formulated as multi-objective function to minimize the error between the estimated and the manufacturer data. Chaotic ant swarm algorithm is a novel optimization method, which has the ability of global optimum search. A numerical simulation on the test motor is conducted. Simulation results show that the proposed method is effective in parameter estimation of the induction motor. Introduction Induction machine models used for the solution of a variety of steady-state problems require equivalent circuit parameters. These parameters include the resistances and reactances representing the stator, rotor and magnetizing branches. The main problem of induction motor parameter estimation is the unavailability of manufacturer data to construct accurate models. Due to this reason, the induction motor models are not explicitly represented in various applications. The conventional technique for estimating the induction motor parameters are based on the no-load and the locked-rotor tests[1]. However, these approaches cannot be implemented easily. Besides, the locked-rotor test requires that the shaft of the motor be locked. In the locked-rotor condition, the frequency of the rotor is equal to the supply frequency, but under typical operations, the rotor frequency is perhaps 1–3Hz. This incorrect rotor frequency will give misleading results for the locked-rotor test. In the recent years, global optimization techniques such as evolutionary algorithm[2], genetic algorithm[3,4], adaptive genetic algorithm[5] and differential evolution[6] have been proposed to solve the parameter estimation problems. Though the genetic algorithm methods have been employed successfully to solve complex non-linear optimization problems, recent research has identified some deficiencies in GA[7]. This degradation in efficiency is apparent when the parameters being optimized are highly correlated and the premature convergence of the GA degrades its performance in terms of reducing the search capability of genetic algorithm(GA). Chaotic ant swarm algorithm was inspired by behaviors of real ants of nature. This novel method includes both effects of chaotic dynamics and swarm-based search. It is a deterministic process different from the conventional ant algorithm. Chaotic ant swarm algorithm has the ability of global optimum search and can generate high-quality solutions. Chaotic Ant Swarm Algorithm Algorithms inspired by models of ant colony behaviors have achieved increasing success among researchers in computer science and operations research. Much of the knowing developed ant-inspired optimization algorithms are based on the random metaheuristic of non-deterministic probability theory. However, it has been discovered by biologists that single ant shows chaotic Proceedings of the 2nd International Conference On Systems Engineering and Modeling (ICSEM-13) Published by Atlantis Press, Paris, France. © the authors, 2013 0200 activity patterns. And from the viewpoint of kinetics, some certain inherent relations between the chaotic activity patterns of single ant and the self-organizing behavior of the colony exist. The chaotic behavior of individual ant and the intelligent organization actions of ant colony are adaptations to the environment. These behaviors are very important for the ant to find food and survive[8,9]. Inspired by natural ants behavior, Reference [10,11] combined the ant chaotic kinetics, swarm organizing, and optimization mechanism ingeniously, and proposed a new kind of optimization algorithm based on swarm intelligent theory, which is chaotic ant swarm algorithm. The mathematic model of the chaotic ant swarm is as follows: |
| File Format | PDF HTM / HTML |
| DOI | 10.2991/icsem.2013.40 |
| Alternate Webpage(s) | https://download.atlantis-press.com/article/5534.pdf |
| Alternate Webpage(s) | https://doi.org/10.2991/icsem.2013.40 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
