Parameter identification of induction motor using modified Particle Swarm Optimization algorithm

Emara, Hassan M.; Elshamy, Wesam; Bahgat, A.

doi:10.1109/isie.2008.4677254

Cited by 23 publications

(8 citation statements)

References 15 publications

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“…However, the information of the real state of the motor and current displacement effect influence on the rotor parameters is not taken into consideration in [11]. The use of genetic algorithms is rather topical but quite a lot of experiments are required to create a genetic model [12]. The paper [13] contains description of a method developed for the online assessment of the stator and rotor resistance with the use of artificial neural networks.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Refined calculation of induction motor equivalent circuit nonlinear parameters by an energy method

Zagirnyak

Rodkin

Romashykhin

et al. 2017

EEJET

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Refined calculation of induction motor equivalent circuit nonlinear parameters by an energy method

Zagirnyak

Rodkin

Romashykhin

et al. 2017

EEJET

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“…Emara, et al, proposed the modified PSO algorithm for induction motor parameter identification in [9]. In [10], Rashag, et al, used the PSO-based RBF neural network to identify the induction motor parameters. Sakthivel, et al, in [11], presented the improved PSO algorithm for induction motor parameter determination.…”

Section: Introductionmentioning

confidence: 99%

“…The results on motor parameters estimation, including stator's impedance and rotor's impedance, were investigated. The mentioned previous works [7][8][9][10][11] indicated that the intelligent optimization techniques can be effectively used for induction motor parameter identification and the PSO algorithm was most effective for estimating the parameters over a wide operating range of the motor. In [11], the improved PSO was used to estimate the parameters of induction motor equivalent circuit.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization Based Equivalent Circuit Estimation for On-Service Three-Phase Induction Motor Efficiency Assessment

Chayakulkheeree¹,

Hengsritawat²,

Nantivatana³

2017

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Abstract. This paper presents the particle swarm optimization based equivalent circuit estimation (PSOBECE) method for three-phase induction motor efficiency analysis, during on-service condition. The prominent point of the paper is to accurately estimate the three-phase induction motor efficiency without disturbing motor operation, using basic electrical instruments, which are clamp-on power meter and non-contact tachometer. The proposed method estimates the parameters of induction motor equivalent circuit (EC) using motor current, real power, voltage, and speed, which can be measured without interrupting the motors operations. Afterward, the motor's efficiency can be nonintrusively determined from the EC. In this paper, the 1.5, 11 and 30 kW motors are used for test and verify the accuracy of the proposed method. The motor efficiency test results, by shaft torque method, compliance to IEC60034 standards, are used as references to verify the accuracy of the proposed method compared to the conventional slip and current methods. The proposed method resulted in the reasonable mismatch in efficiency estimation for three-phase induction motor. The root-mean square mismatch of the proposed method, comparing to those of obtained by CM and SM, is shown to be minimum. Therefore, the proposed method could effectively and conveniently be applied for the on-service three-phase induction motor efficiency estimations.

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“…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%

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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Parameter identification of induction motor using modified Particle Swarm Optimization algorithm

Cited by 23 publications

References 15 publications

Refined calculation of induction motor equivalent circuit nonlinear parameters by an energy method

Refined calculation of induction motor equivalent circuit nonlinear parameters by an energy method

Particle Swarm Optimization Based Equivalent Circuit Estimation for On-Service Three-Phase Induction Motor Efficiency Assessment

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

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