Identification of Induction Machine Parameters Using a New Adaptive Genetic Algorithm

Abdelhadi, B.; Benoudjit, A.; Naït-Saïd, Nasreddine

doi:10.1080/15325000490466645

Cited by 29 publications

(16 citation statements)

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“…In the recent years, global optimization techniques such as evolutionary algorithm (Nangsue et al, 1999), genetic algorithm (Bishop and Richards, 1990;Alonge et al, 1998;Pillay et al, 1997;Rahimpour et al, 2007;Huang et al, 2001;Nollan et al, 1994;Orlowska Kowalska et al, 2006), adaptive GA (Abdelhadi et al, 2004) and differential evolution (Ursem and Vadstrup, 2003) have been proposed to solve the parameter estimation problems.…”

Section: Article In Pressmentioning

confidence: 99%

Multi-objective parameter estimation of induction motor using particle swarm optimization

Sakthivel¹,

Ramachandran²,

Subramanian³

2010

Engineering Applications of Artificial Intelligence

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Section: Article In Pressmentioning

confidence: 99%

Multi-objective parameter estimation of induction motor using particle swarm optimization

Sakthivel¹,

Ramachandran²,

Subramanian³

2010

Engineering Applications of Artificial Intelligence

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“…Evolutionary algorithms such as genetic algorithm (GA) [6][7][8][9][10][11][12], adaptive GA [13], evolutionary algorithm (EA) [14] and differential evolution [15] were applied for parameter identification and efficiency estimation problems. Though the GA methods have been employed successfully to solve complex non-linear optimization problems, recent research has identified some deficiencies in GA performance [16].…”

Section: Introductionmentioning

confidence: 99%

Swarm Intelligence Algorithm for Induction Motor Field Efficiency Evaluation

Sakthivel

Subramanian

2010

Swarm, Evolutionary, and Memetic Computing

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Abstract. Determining induction motor field efficiency is imperative in industries for energy conservation and cost savings. The induction motor efficiency is generally tested in a laboratories by certain methods defined in IEEE Standard -112. But these methods cannot be used for motor efficiency evaluations in the field because it disrupts the production process of the industry. This paper proposes a swarm intelligence algorithm, Particle Swarm Optimization (PSO) for efficiency evaluation of in-service induction motor based on a modified induction motor equivalent circuit model. In this model, stray load losses are considered. The proposed efficiency evaluation method combines the PSO and the equivalent circuit method. First, the equivalent circuit parameters are estimated by minimizing the difference between measured and calculated values of stator current and input power of the motor using the PSO algorithm. Based on these parameters, the efficiency of the motor at various load points are evaluated by using the equivalent circuit method. To exemplify the performance of the PSO based efficiency estimation method, a 5 HP motor has been tested, compared with genetic algorithm (GA), torque gauge method, equivalent circuit method, slip method, current method and segregated loss method and found to be superior. Accordingly, the method will be useful for engineers who implement the energy efficiency programs to the electric motor systems in industries.

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Section: Introductionmentioning

confidence: 99%

“…This incorrect rotor frequency will give misleading results for the locked-rotor test. In the recent years, global optimization techniques such as evolutionary algorithm [2], genetic algorithm [3,4], adaptive genetic algorithm [5] and differential evolution [6] have been proposed to solve the parameter estimation problems.Though the genetic algorithm methods have been employed successfully to solve complex non-linear optimization problems, recent research has identified some deficiencies in GA [7]. This degradation in efficiency is apparent when the parameters being optimized are highly correlated and the premature convergence of the GA degrades its performance in terms of reducing the search capability of genetic algorithm(GA).…”

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confidence: 99%

Chaotic Ant Swarm Based Parameter Estimation of Induction Motor from Manufacturer Data

Li¹,

Jia²

2013

Proceedings of the 2nd International Conference on Systems Engineering and Modeling

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Abstract.A method of parameter estimation of induction motor based on optimization using a chaotic swarm algorithm is presented. The parameter estimation methodology describes a method for estimating the steady-state equivalent circuit parameters from the motor performance characteristic, which is normally available from manufacturer data or from tests. The optimization problem is formulated as multi-objective function to minimize the error between the estimated and the manufacturer data. Chaotic ant swarm algorithm is a novel optimization method, which has the ability of global optimum search. A numerical simulation on the test motor is conducted. Simulation results show that the proposed method is effective in parameter estimation of the induction motor. IntroductionInduction machine models used for the solution of a variety of steady-state problems require equivalent circuit parameters. These parameters include the resistances and reactances representing the stator, rotor and magnetizing branches. The main problem of induction motor parameter estimation is the unavailability of manufacturer data to construct accurate models. Due to this reason, the induction motor models are not explicitly represented in various applications.The conventional technique for estimating the induction motor parameters are based on the no-load and the locked-rotor tests [1]. However, these approaches cannot be implemented easily. Besides, the locked-rotor test requires that the shaft of the motor be locked. In the locked-rotor condition, the frequency of the rotor is equal to the supply frequency, but under typical operations, the rotor frequency is perhaps 1-3Hz. This incorrect rotor frequency will give misleading results for the locked-rotor test. In the recent years, global optimization techniques such as evolutionary algorithm [2], genetic algorithm [3,4], adaptive genetic algorithm [5] and differential evolution [6] have been proposed to solve the parameter estimation problems.Though the genetic algorithm methods have been employed successfully to solve complex non-linear optimization problems, recent research has identified some deficiencies in GA [7]. This degradation in efficiency is apparent when the parameters being optimized are highly correlated and the premature convergence of the GA degrades its performance in terms of reducing the search capability of genetic algorithm(GA).Chaotic ant swarm algorithm was inspired by behaviors of real ants of nature. This novel method includes both effects of chaotic dynamics and swarm-based search. It is a deterministic process different from the conventional ant algorithm. Chaotic ant swarm algorithm has the ability of global optimum search and can generate high-quality solutions.

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Identification of Induction Machine Parameters Using a New Adaptive Genetic Algorithm

Cited by 29 publications

References 0 publications

Multi-objective parameter estimation of induction motor using particle swarm optimization

Multi-objective parameter estimation of induction motor using particle swarm optimization

Swarm Intelligence Algorithm for Induction Motor Field Efficiency Evaluation

Chaotic Ant Swarm Based Parameter Estimation of Induction Motor from Manufacturer Data

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