Induction machine parameter identification: A comparison between GAs and PSO approaches

Yousfi, Laatra; Bouchemha, Amel; Bechouat, Mohcene; Boukrouche, Abdelhani

doi:10.1109/ever.2013.6521561

Cited by 8 publications

(10 citation statements)

References 7 publications

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“…Actually, related works [4,5,7,9] describe an identification of the induction machine parameters which is almost impossible with only one objective. The best results presented in [7,9] are not optimal and their parameters variation is close to zero.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, we use for the first time the new -NSGA III algorithm in order to estimate the induction machine parameters. Moreover, we show that GAs give better identification than PSO algorithms used in [9] and this depends on the GAs settings. Finally, we can also identify the part of the reference signal which has the biggest error due to the usage of the multi-objective case.…”

Section: Introductionmentioning

confidence: 87%

“…Indeed, GAs, Multi-Objective GAs and Particle Swarm Optimization (PSO) are the most encountered algorithms. Several works [9][10][11] assert that PSO algorithms give the best results to identify parameters but the difference between GAs and PSO are insignificant in these works. Nevertheless, in order to perform an effective control of the motor, the machine parameters have to be known with accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An effective identification of the induction machine parameters using a classic genetic algorithm, NSGA II and θ-NSGA III

Maître

Gaboury

Bouchard

et al. 2015

2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA)

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To remain competitive, the manufacturing industry is using computer processing power to innovate, develop and optimize new cost-efficient production strategies. This is the reason why optimization of automation systems is deployed to improve productivity, quality and robustness of the production. The different existing goals of optimization as the control machine, management of the power consumption, design of electrical installation and prediction of motor faults lead to the necessity of estimating the induction machine parameters (the stator and rotor resistances, the stator and rotor inductances and the magnetizing inductance). To these ends, researchers and companies are investigating efficient methods to identify these parameters. In this paper, we propose an effective method for the induction machine parameters identification based on the new -NSGA III genetic algorithm. A comparison between a classic single objective genetic algorithm (GA) and two well-known multiobjectives GAs (NSGA II and -NSGA III) is performed. Our results show that the multi-objective GA -NSGA III provides a better estimation of parameters than the classic single objective GA and the multi-objective GA NSGA II.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An effective identification of the induction machine parameters using a classic genetic algorithm, NSGA II and θ-NSGA III

Maître

Gaboury

Bouchard

et al. 2015

2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA)

View full text Add to dashboard Cite

show abstract

“…They have shown that both algorithms can find appropriate parameters against the errors caused by machine stator. [6] Chunyuan Bian et al carried out parameter identification application in the control of the induction motor. They have used three level inverter in the control circuit and DSP is used in experimental study.…”

Section: Introductionmentioning

confidence: 99%

System identification and control of an wound rotor AC induction machine for wind turbine

Çolak

Bülbül

Şahin

2013

2013 International Conference on Renewable Energy Research and Applications (ICRERA)

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In this study a wound rotor AC induction machine which can be used in wind turbine is modeled in LabVIEW program and the system model optimization is done with system identification (SI) method. Wind turbines are usually used as synchronous and asynchronous. In this study, to be safe and because of the advantages such as low costs, WRIM is preferred. In the control of the excitation circuit, SI method is used to provide more safe, effective and efficient control. With this method, control parameters are optimized to be used on the system. In simulations real wind data are used as input to approach reality.

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“…Since the fluxes on α and β axes are difficult to determine theoretically based on voltage and current in real-time applications, artificial neural networks [1], genetic algorithm (GA) [2][3][4][5], or particle swarm optimization (PSO) [6][7][8] and algorithm comparison (GA-PSO, GA-modified GA, PSO-modified PSO, GA-cuckoo alg., etc.) [9][10][11][12][13][14] can be used. Heuristic algorithms afford relatively easy numeric solutions for problems difficult to be solved theoretically.…”

Section: Introductionmentioning

confidence: 99%

Heuristic algorithm-based estimation of rotor resistance of an induction machine by slot parameters with experimental verification

Çelebi¹,

Toren²

2017

Turk J Elec Eng & Comp Sci

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Abstract:The estimations of induction machine equivalent circuit parameters are still being widely used in the analysis and in determining the characteristics of the machine. Since the most important part of the machine is the rotor where torque is produced, the calculation of rotor resistance correctly will directly affect all other data. Almost all parameters belonging to the stator side can easily be determined through external measurements. However, due to the formulation of the rotor as a closed box, estimating rotor resistance and the rotor's slot shape by heuristic algorithms, without damaging the rotor physically, and comparing it with its actual value constitutes the first focus of this study. In this regard, rotor resistance and slot parameters are estimated through heuristic algorithms depending on the induction machine design aspects. Secondly, an improved particle swarm optimization is presented and compared with conventional PSO and genetic algorithm.

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Induction machine parameter identification: A comparison between GAs and PSO approaches

Cited by 8 publications

References 7 publications

An effective identification of the induction machine parameters using a classic genetic algorithm, NSGA II and θ-NSGA III

An effective identification of the induction machine parameters using a classic genetic algorithm, NSGA II and θ-NSGA III

System identification and control of an wound rotor AC induction machine for wind turbine

Heuristic algorithm-based estimation of rotor resistance of an induction machine by slot parameters with experimental verification

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