Multi-objective parameter estimation of induction motor using particle swarm optimization |
| |
| Authors: | VP Sakthivel R Bhuvaneswari S Subramanian |
| |
| Affiliation: | 1. Department of Computer Science, Electrical and Space Engineering, Division of Signals and Systems, Luleå University of Technology, Sweden;2. Department of Electrical Engineering, Collage of Engineering, University of Mosul, Mosul, Iraq;1. Dept. of Electrical and Electronics Engineering, Shamoon College of Engineering, Israel;2. Dept. of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Israel;3. Dept. of Electrical Engineering and Electronics, Ariel University, Israel;1. LMSE Laboratory, Department of Electrical Engineering, University of Biskra, Algeria;2. LGEB Laboratory, Department of Electrical Engineering, University of Biskra, Algeria;3. LGEB Laboratory, Department of Electrical Engineering, University El-Oued, Algeria |
| |
| Abstract: | In order to simplify the offline parameter estimation of induction motor, a method based on optimization using a particle swarm optimization (PSO) technique is presented. Three different induction motor models such as approximate, exact and deep bar circuit models are considered. The parameter estimation methodology describes a method for estimating the steady-state equivalent circuit parameters from the motor performance characteristics, which is normally available from the 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. The sensitivity analysis is also performed to identify parameters, which have the most impact on motor performance. The feasibility of the proposed method is demonstrated for two different motors and it is compared with the genetic algorithm and the classical parameter estimation method. Simulation results show that the proposed PSO method was indeed capable of estimating the parameters over a wide operating range of the motor. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
