Parameter identification of three-phase induction motor using MATLAB-simulink

Salimin, Rahmatul Hidayah; Kadir, Siti Hajar Binti Abd; Mohd, Shah Rizam; Baki, Shah Rizam Mohd Shah; Ismail, Faridah

doi:10.1109/peoco.2013.6564627

Cited by 11 publications

(7 citation statements)

References 5 publications

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“…Measurements and data collection are carried out for the following parameters: currents and voltage of the stator phase winding, rotational speed and angular acceleration. The Generalized Theory of Electrical Machines (GEM) is used to cover a wide range of electrical machines in a unified manner [10]- [15].…”

Section: Induction Motor Drive With Identification Devicementioning

confidence: 99%

Identification of the Parameters of a Three-Phase Asynchronous Motor for Intelligent Monitoring Systems

Makarov

Zagirova

Vagapov

et al. 2022

Latvian Journal of Physics and Technical Sciences

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The study presents an algorithm and a mathematical model for evaluating the parameters of the equivalent circuit of an asynchronous electric motor for intelligent monitoring systems. To identify the parameters of electric motors, a gradient descent method is used to find the minimum of a positive function. The algorithm and mathematical model were tested not only theoretically, but also in laboratory experiments. The results obtained proved that the proposed algorithm was an efficient and accurate method for estimating parameters. Computer modelling and experimental research confirmed the possibility of using the algorithm and the device for identifying parameters in the construction of control systems for a variable frequency drive with three-phase asynchronous electric motors in practice.

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Section: Induction Motor Drive With Identification Devicementioning

confidence: 99%

Identification of the Parameters of a Three-Phase Asynchronous Motor for Intelligent Monitoring Systems

Makarov

Zagirova

Vagapov

et al. 2022

Latvian Journal of Physics and Technical Sciences

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“…There are a large number of works (Babau et al, 2007;P. Kumar et al, 2014;Salimin et al, 2013;Vieira et al, 2010;Yazid et al, 2011;Yousfi et al, 2013), which deal with identification parameters of the induction machine. As briefly mentioned in the introduction, two major trends of research can be found.…”

Section: Related Workmentioning

confidence: 99%

“…There are two major approaches to obtain these parameters. The first one is based on off-line techniques (external measurements, optimization algorithms) (Babau, Boldea, Miller, & Muntean, 2007; P. Kumar, Dalal, & Singh, 2014;Salimin et al, 2013), and the second relies on on-line methods (Recursive Least Square, Extended Kalman Filter…) (Vieira, Azzolin, Gastaldini, & Gründling, 2010;Yazid, Bouhoune, Menaa, & Larabi, 2011). In this paper, is the authors' focus is on the optimization algorithms, and more specifically on the Evolutionary Algorithms (EAs).…”

Section: Introductionmentioning

confidence: 99%

A Black-Box Model for Estimation of the Induction Machine Parameters Based on Stochastic Algorithms

Maître

Gaboury

Bouchard

et al. 2015

International Journal of Monitoring and Surveillance Technologies Research

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Knowledge on asynchronous machine parameters (resistances, inductances…) has become necessary for the manufacturing industry in the interest of optimizing performances in a production system (roll-to-roll processing, wind generator…). Indeed, accurate values of this machine allow improving control of the torque, speed and position, managing power consumption in the best way possible, and predicting induction machine failures with great effectiveness. In these regards, the authors of this paper propose a black-box modeling for a powerful identification of asynchronous machine parameters relying on stochastic research algorithms. The algorithms used for the estimation process are a single objective genetic algorithm, the well-known NSGA II and the new ?-NSGA III (multi-objective genetic algorithms). Results provided by those show that the best estimation of asynchronous machines parameters is given by ?-NSGA III. In addition, this outcome is confirmed by performing the identification process on three different induction machines.

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“…In scientific literature, various methods and different software have been described to determine AC motor parameters [1–39]. However, a universal solution to this problem is not achieved yet.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, the applicability of such coefficients is restricted for a wide range of motors. For the sake of justice, in this group of methods should be mentioned analytical approaches are using during the on‐line or special laboratory tests [13–25]. As the main disadvantage, the need for a correction coefficient to improve precision for different motors, the restricted precision and inherent instability of results are typical for most of the analytical methods from this group.…”

Section: Introductionmentioning

confidence: 99%

Simplified algorithm for assessment equivalent circuit parameters of induction motors

Rajput

Bender

Averbukh

2020

IET Electric Power Applications

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Induction or asynchronous AC motors are widely used in the electromechanical industries. However, the selection of an appropriate motor for specific drive applications represents a non-trivial task. The most effective and universally accepted method is to use equivalent T-circuit for this purpose. Parameter determination of equivalent circuit should be done relatively fast and accurately. This article describes a novel and simplified method for the estimation of equivalent circuit parameters, which is exclusively based on the manufacturer's datasheet. The proposed method is based on the synergetic interaction between the numerical and analytical dimensionless approach using the Thevenin theorem. Initially, rated motor parameters and unknown variables of the equivalent circuit are combined into dimensionless expressions using the Thevenin approach. This method is simple, original, and useful to prevent instability in the numerical solution. Importantly, the high convergence of a solution and relatively fast calculation of parameters reveals the significant novelty of the proposed method. This method provides accurate and sustainable results for a wide range of low voltage asynchronous motors with different rotational velocities, torque, and power range from a few to hundreds of kilowatts. The proposed method is validated by the application of three types of asynchronous AC motors.

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Parameter identification of three-phase induction motor using MATLAB-simulink

Cited by 11 publications

References 5 publications

Identification of the Parameters of a Three-Phase Asynchronous Motor for Intelligent Monitoring Systems

Identification of the Parameters of a Three-Phase Asynchronous Motor for Intelligent Monitoring Systems

A Black-Box Model for Estimation of the Induction Machine Parameters Based on Stochastic Algorithms

Simplified algorithm for assessment equivalent circuit parameters of induction motors

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