On Line Parameter Identification of an Induction Motor Using Improved Particle Swarm Optimization

Chen, Guangyi; Wei, Guo; Huang, Kaisheng

doi:10.1109/chicc.2006.4347151

Cited by 13 publications

(7 citation statements)

References 8 publications

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“…This algorithm is simpler and easier to implement than other evolutionary algorithms (GA, LSA, SA, or ES) [2][3][4][5], as it only has a few parameters to adjust. So far, there have been researchers who have used the PSO with various variants for parameter estimation such as diversity-guided particle swarm optimization (DGPSO) [5], stretching particle swarm optimization (SPSO) [8], particle swarm optimization with a constriction factor [6], and particle swarm optimization with a time-varying inertia weight [7]. Another variant of the PSO, dynamic particle swarm optimization (Dynamic PSO) with time-varying acceleration coefficients of the cognitive and social components has been described recently [9][10][11].…”

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%

“…The parameter estimation objective is transformed into an optimization problem. 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.…”

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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…Particle swarm optimization (PSO) algorithm is another approach which has been successfully customized for parameter estimation and due to its inherent simplicity it has been much popular than other known optimization techniques. PSO itself has different variants such as diversity-guided particle swarm optimization [11], particle swarm optimization with a constriction factor [12], particle swarm optimization with a time-varying inertia weight [13], dynamic particle swarm optimization with time-varying acceleration coefficients of the cognitive and social components [14] and a stretching particle swarm optimization [15]. These methods basically differ in concept of how to regulate the search and optimization evolution process.…”

Section: Introductionmentioning

confidence: 99%

Online estimation of induction motor parameters using a modified particle swarm optimization technique

Tofighi

Mahdizadeh

Feyzi

2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

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This paper addresses an application of Particle Swarm Optimization algorithm to dynamically estimate and track the changes in parameters of an induction Motor in steady-state condition. In real-time operation, the performance of the control system is influenced by various environmental and internal factors. The former studies used the data in offline condition of the motor to estimate the parameters. In this novel method however, the measured three-phase currents, voltages and the speed of the induction machine are used as inputs and the effect of the temperature rise on the motor parameters; i.e., rotor and stator resistances is investigated implementing a two-stage single-flock particle swarm optimization technique. The simulation is based on a proper model of the induction motor, including electromagnetic and mechanical elements. The parameters are calculated and the estimation errors are minimized via a normalized root mean square error measure respectively.

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“…But the linear method can't be fit for much better value of the searching step and searching precision. In [8] is presented a nonlinear modulation index dynamic inertia weight modified particle swarm optimization. Although this method can improve optimized performance of particle swarm optimization, the best value of nonlinear modulation index can't be chosen expediently.…”

Section: Introductionmentioning

confidence: 99%

A New Method of Speed Control for Induction Motor Based On Improved Particle Swarm Optimization

Yi¹,

Sui²,

Yu³

2016

JCTECS

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Abstract-Optimization techniques are becoming more popular for the improvement in control of induction motor. Many intelligent algorithms have been used to improve performance of induction motor so for including particle swarm optimization. However, the improved performance may be limited on account of inertia coefficient in particle swarm optimization, which lead to the unbalance between the searching step and searching precision. In this paper, a variable-step nonlinear dynamic inertia weight of particle swarm optimization speed controller is proposed to improve the performance of an induction motor. The experiment results show that the proposed method has excellent performance.

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On Line Parameter Identification of an Induction Motor Using Improved Particle Swarm Optimization

Cited by 13 publications

References 8 publications

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

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

Online estimation of induction motor parameters using a modified particle swarm optimization technique

A New Method of Speed Control for Induction Motor Based On Improved Particle Swarm Optimization

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