Parameter Estimation of Three-Phase Induction Motor Using Hybrid of Genetic Algorithm and Particle Swarm Optimization
A cost effective off-line method for equivalent circuit parameter estimation of an induction motor using hybrid of genetic algorithm and particle swarm optimization (HGAPSO) is proposed. The HGAPSO inherits the advantages of both genetic algorithm (GA) and particle swarm optimization (PSO). The para...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Journal of Engineering |
| Online Access: | http://dx.doi.org/10.1155/2014/148204 |
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| author | Hamid Reza Mohammadi Ali Akhavan |
| author_facet | Hamid Reza Mohammadi Ali Akhavan |
| author_sort | Hamid Reza Mohammadi |
| collection | DOAJ |
| description | A cost effective off-line method for equivalent circuit parameter estimation of an induction motor using hybrid of genetic algorithm and particle swarm optimization (HGAPSO) is proposed. The HGAPSO inherits the advantages of both genetic algorithm (GA) and particle swarm optimization (PSO). 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 nameplate data or experimental tests. In this paper, the problem formulation uses the starting torque, the full load torque, the maximum torque, and the full load power factor which are normally available from the manufacturer data. The proposed method is used to estimate the stator and rotor resistances, the stator and rotor leakage reactances, and the magnetizing reactance in the steady-state equivalent circuit. The optimization problem is formulated to minimize an objective function containing the error between the estimated and the manufacturer data. The validity of the proposed method is demonstrated for a preset model of induction motor in MATLAB/Simulink. Also, the performance evaluation of the proposed method is carried out by comparison between the results of the HGAPSO, GA, and PSO. |
| format | Article |
| id | doaj-art-27ea62fa29434a20aa6a88b4a093e6db |
| institution | OA Journals |
| issn | 2314-4904 2314-4912 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Engineering |
| spelling | doaj-art-27ea62fa29434a20aa6a88b4a093e6db2025-08-20T02:04:19ZengWileyJournal of Engineering2314-49042314-49122014-01-01201410.1155/2014/148204148204Parameter Estimation of Three-Phase Induction Motor Using Hybrid of Genetic Algorithm and Particle Swarm OptimizationHamid Reza Mohammadi0Ali Akhavan1Department of Electrical and Computer Engineering, University of Kashan, Ravand Street, P.O. Box 87317-51167, Kashan, IranDepartment of Electrical and Computer Engineering, University of Kashan, Ravand Street, P.O. Box 87317-51167, Kashan, IranA cost effective off-line method for equivalent circuit parameter estimation of an induction motor using hybrid of genetic algorithm and particle swarm optimization (HGAPSO) is proposed. The HGAPSO inherits the advantages of both genetic algorithm (GA) and particle swarm optimization (PSO). 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 nameplate data or experimental tests. In this paper, the problem formulation uses the starting torque, the full load torque, the maximum torque, and the full load power factor which are normally available from the manufacturer data. The proposed method is used to estimate the stator and rotor resistances, the stator and rotor leakage reactances, and the magnetizing reactance in the steady-state equivalent circuit. The optimization problem is formulated to minimize an objective function containing the error between the estimated and the manufacturer data. The validity of the proposed method is demonstrated for a preset model of induction motor in MATLAB/Simulink. Also, the performance evaluation of the proposed method is carried out by comparison between the results of the HGAPSO, GA, and PSO.http://dx.doi.org/10.1155/2014/148204 |
| spellingShingle | Hamid Reza Mohammadi Ali Akhavan Parameter Estimation of Three-Phase Induction Motor Using Hybrid of Genetic Algorithm and Particle Swarm Optimization Journal of Engineering |
| title | Parameter Estimation of Three-Phase Induction Motor Using Hybrid of Genetic Algorithm and Particle Swarm Optimization |
| title_full | Parameter Estimation of Three-Phase Induction Motor Using Hybrid of Genetic Algorithm and Particle Swarm Optimization |
| title_fullStr | Parameter Estimation of Three-Phase Induction Motor Using Hybrid of Genetic Algorithm and Particle Swarm Optimization |
| title_full_unstemmed | Parameter Estimation of Three-Phase Induction Motor Using Hybrid of Genetic Algorithm and Particle Swarm Optimization |
| title_short | Parameter Estimation of Three-Phase Induction Motor Using Hybrid of Genetic Algorithm and Particle Swarm Optimization |
| title_sort | parameter estimation of three phase induction motor using hybrid of genetic algorithm and particle swarm optimization |
| url | http://dx.doi.org/10.1155/2014/148204 |
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