Induction motor parameter estimation using metaheuristic methods

Çanakoğlu, Ali İhsan; Yetgin, Asım Gökhan; Temurtaş, Hasan; Turan, Mustafa

doi:10.3906/elk-1211-171

Cited by 28 publications

(20 citation statements)

References 34 publications

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“…Steady state methods use simplified motor models to solve the parameter estimation problem 194 but require multiple tests measurements at different loading conditions. Optimization techniques that are inspired by the phenomenon of natural evolution and Swarm Intelligence (SI) have been applied for IM parameter estimation [15][16][17]. These methods rely only on measurements of the motor terminal voltages and currents under steady state operation.…”

Section: Introductionmentioning

confidence: 99%

A parameter estimation algorithm for induction machines using Artificial Bee Colony (ABC) optimization

Aminu

2019

Nig. J. Tech.

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Conventionally, parameters of the Induction Motor (IM) are determined using the standard noload and locked rotor test. Performing the no-load test is simple and involved running the machine uncoupled to a load, while measuring the power, voltage, current and shaft speed at different voltage test points. On the other hand, the locked rotor test requires full control of the rotor mechanically in the locked condition before measurements are taken. This paper presents a method for estimating the parameters of IMs without the need for the no-load and locked rotor tests. The method is based on optimization approach using a relatively new swarm based algorithm called the Artificial Bee Colony (ABC) optimization. Two different equivalent circuits are implemented for the parameter estimation scheme; one with parallel and the other with series magnetization circuit. Parameters of a standard 7.5kW IM are estimated using the measured and estimated stator current, input and output power and the power factor. Based on the experimental results obtained, the optimization method using the ABC algorithm gave accurate estimates of the IM parameters when compared to the reference parameters determined using the IEEE standard 112-2004. The maximum errors of -13.730% and 2.249% are obtained for the parallel and series equivalent circuits respectively.

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Section: Introductionmentioning

confidence: 99%

A parameter estimation algorithm for induction machines using Artificial Bee Colony (ABC) optimization

Aminu

2019

Nig. J. Tech.

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“…The six EC parameters, with slip affect, indicated in Fig. 2, are as follows [3] The set of populations in PSO [12] formulation can be expressed as, Thereafter, the PSO algorithm [12] is used to solve the minimization problem. The PSOBECE computational procedure can be illustrated as follows:…”

Section: Psobece Problem Formulationmentioning

confidence: 99%

“…Meanwhile, the efficiency determined by the obtained parameters was not investigated. Therefore, the information on induction motor parameters can be further investigated for induction motor efficiency estimation [12][13]. This paper, therefore, proposes the on-service three-phase induction motor efficiency estimation using the particle swarm optimization based equivalent circuit estimation (PSOBECE).…”

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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“…The method of finite element analysis was employed in [4]. Also, parameter estimation of an induction motor using some evolutionary algorithms is presented in [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of Three-Phase Induction Motor Using Hybrid of Genetic Algorithm and Particle Swarm Optimization

Mohammadi

Akhavan

2014

Journal of Engineering

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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 steadystate 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.

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Induction motor parameter estimation using metaheuristic methods

Cited by 28 publications

References 34 publications

A parameter estimation algorithm for induction machines using Artificial Bee Colony (ABC) optimization

A parameter estimation algorithm for induction machines using Artificial Bee Colony (ABC) optimization

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

Parameter Estimation of Three-Phase Induction Motor Using Hybrid of Genetic Algorithm and Particle Swarm Optimization

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