Multiobjective genetic estimation to induction motor parameters

Sağ, Tahir; Çunkaş, Mehmet

doi:10.1109/acemp.2007.4510580

Cited by 24 publications

(12 citation statements)

References 5 publications

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“…These include: non-linear least squares, Kalman Filters [5], genetic algorithms (GA) [6]- [8], local search algorithms (LSA), simulated annealing (SA), differential evolution and various forms of particle swarm optimization (PSO) [9]- [12]. Most of these techniques are applied to data gathered during the startup of the machine [7], [13], [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Online estimation of Equivalent Model for cluster of induction generators: A MVMO-based approach

Rueda

González-Longatt

Erlich

2015

2015 18th International Conference on Intelligent System Application to Power Systems (ISAP)

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Section: Introductionmentioning

confidence: 99%

Online estimation of Equivalent Model for cluster of induction generators: A MVMO-based approach

Rueda

González-Longatt

Erlich

2015

2015 18th International Conference on Intelligent System Application to Power Systems (ISAP)

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“…Most of these techniques formulate the problem as a nonlinear least square minimisation problem that estimates all the unknown parameters simultaneously. The least square minimisation problem can be solved either by traditional optimization methods [13], [20]- [22] or by Genetic Algorithms (GAs) [16]- [19], [23]. A fundamental drawback of traditional optimization techniques is their dependence on unrealistic assumptions, such as unimodal performance landscapes and differentiability of the performance function [24].…”

Section: Introductionmentioning

confidence: 99%

Identification of three phase induction machines equivalent circuits parameters using multi-objective genetic algorithms

Kumar

Dalal

Singh

2014

2014 International Conference on Electrical Machines (ICEM)

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The use of multiobjective optimization technique in determining the values of the steady-state equivalent circuit parameters of a three-phase squirrel-cage induction machine is discussed. The identification procedure is based on the steady state phase current versus slip and torque versus slip characteristics. Nonlinearities in the Induction machine such as saturation effects and skin effects are also taken into account. The proposed technique is based on Multiobjective Genetic Algorithms (MOGA). The MOGA is used to minimize the error between the actual data and the data obtained by equivalent circuit. The robustness of the method is shown by identifying parameters of the induction motor in three different cases. The simulation results show that the method successfully estimates the motor parameters.

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“…This is a difficult problem since the mathematical model of an induction machine is non-linear and there are several state variables which cannot be measured directly, such as the rotor flux. However, this problem has been resolved using optimization techniques such as the genetic algorithm (GA) [2][3][4], a local search algorithm (LSA), a simulated annealing (SA) approach, and an evolution strategy (ES) [5]. The parameter estimation objective is transformed into an optimization problem.…”

Section: Introductionmentioning

confidence: 99%

“…In [2], [5][6][7], the parameter estimation problem is considered during the start-up phase using the non-linear differential equations. In [4], a mathematical model based on three torque functions of an induction machine which are the full load, locked-rotor, and breakdown torques of an equivalent circuit is used for parameter estimation. In [8], the transient operation of an induction machine from standstill to a steady-state speed for a set period followed by successive free motion to standstill is considered.…”

Section: Introductionmentioning

confidence: 99%

“…In [3], parameter estimation is performed using various machine load tests on the steady-state equivalent circuit. Simulations, rather than experimental tests, were performed to estimate the induction machine parameters in [2][3][4][5][6][7][8]. Recently, a particle swarm optimization (PSO) algorithm has been introduced as one of the optimization techniques used for parameter estimation.…”

Section: Introductionmentioning

confidence: 99%

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Parameter estimation of an induction machine using a dynamic particle swarm optimization algorithm

Huynh

Dunnigan

2010

2010 IEEE International Symposium on Industrial Electronics

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This paper proposes a new application of a dynamic particle swarm optimization (PSO) algorithm for parameter estimation of an induction machine. The dynamic PSO is one of the PSO variants, which modifies the acceleration coefficients of the cognitive and social components in the velocity update equation of the PSO as linear time-varying parameters. The acceleration coefficients are varied during the evolution process of the PSO to improve the global search capability of particles in the early stage of the optimization process and direct the global optima at the end stage. The algorithm uses the measurements of the three-phase stator currents, voltages, and the speed of the induction machine as the inputs to the parameter estimator. The experimental results obtained compare the estimated parameters with the induction machine parameters achieved using traditional tests such as the dc, no-load, and locked-rotor tests. There is also a comparison of the solution quality between a genetic algorithm (GA), standard PSO, and dynamic PSO. The results show that the dynamic PSO is better than the standard PSO and GA for parameter estimation of the induction machine.

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Multiobjective genetic estimation to induction motor parameters

Cited by 24 publications

References 5 publications

Online estimation of Equivalent Model for cluster of induction generators: A MVMO-based approach

Online estimation of Equivalent Model for cluster of induction generators: A MVMO-based approach

Identification of three phase induction machines equivalent circuits parameters using multi-objective genetic algorithms

Parameter estimation of an induction machine using a dynamic particle swarm optimization algorithm

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