Parameter identification of induction motors using stochastic optimization algorithms

Ursem, Rasmus K.; Vadstrup, P.

doi:10.1016/j.asoc.2003.08.002

Cited by 68 publications

(45 citation statements)

References 15 publications

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“…Thus, the fitness function can be used to guide the search towards improved estimation. Consequently, the identification problem is transformed into an optimization problem and now can be formulated as [1] arg min…”

Section: Problem Formulationmentioning

confidence: 99%

“…A fundamental part of engineering applications in systems simulation and control relates to system models, and considerable effort has been devoted towards developing methods to identify precise models together with accurate estimation of system parameters [1]. To date a wide range of analytical techniques have been introduced to meet these demands [2].…”

Section: Introductionmentioning

confidence: 99%

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Parameter identification of a cage induction motor using particle swarm optimization

Nikranajbar

Ebrahimi

Wood

2010

Proceedings of the Institution of Mechanical Engineers, Part I:

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The current paper presents an adaptive system identification/parameter estimation algorithm for a three-phase cage induction motor based on particle swarm optimization (PSO). The performance of the proposed algorithm is emphasized by comparing its results with those of the well-known stochastic optimization techniques of genetic algorithm (GA) and simulated annealing (SA) for the benchmark application with six unknown parameters to identify. The dynamic inertia-weighted PSO algorithm significantly outperformed the GA and SA techniques. The achievement of the presented methodology in confronting a rather complicated non-linear dynamic engineering application underlines the ability of the algorithm to be used for a range of real-world problems, and moreover justifies and motivates the development of more advanced techniques.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parameter identification of a cage induction motor using particle swarm optimization

Nikranajbar

Ebrahimi

Wood

2010

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…These optimization algorithms resulted in far superior performances as compared to the conventional linear and nonlinear iterative methods [9]. Moreover, the authors in [10] rigorously compared the bioinspired optimization algorithms with a few conventional iterative methods for induction motor parameter estimation. Their work finally concluded that bioinspired optimization algorithms are far superior to conventional iterative methods.…”

Section: Introductionmentioning

confidence: 99%

Multiple-global-best guided artificial bee colony algorithm for induction motor parameter estimation

Abro¹,

Mohamad–Saleh²

2014

Turk J Elec Eng & Comp Sci

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Abstract:An induction motor is the most commonly used motor in industry today. Motor circuit parameters are essential for designing, evaluating performance, and controlling the applications of the motor. However, it is difficult to measure the electric parameters, e.g., resistance and reactance, of induction motors accurately. Therefore, researchers have noted the parameter estimation of induction motors as an essential optimization problem. The artificial bee colony (ABC) algorithm is an efficient element of bioinspired optimization algorithms and has been successfully applied in numerous engineering applications. However, the ABC algorithm suffers from slow convergence and poor exploitation. Additionally, there are bleak chances of getting a fitter food source for scout bees using the the standard ABC algorithm scheme.Therefore, different solutions have already been proposed to avoid the flaws of the ABC algorithm. Nevertheless, the proposed solutions are either computationally intensive or prone to local optima traps or they require additional control variables to tune. Moreover, there is no systematic way to tune the additional control variables for yielding the optimal performance of the algorithms. Therefore, this research work proposes a novel variant of the ABC algorithm, which capitalizes on multiple global-best food sources rather than a single global-best food source. In addition, this research work proposes a novel scheme for enhancing the performance of the ABC algorithm's scout bee. Two modifications for the performance enhancement of the ABC algorithm are proposed in this research work. The proposed algorithm is compared with various recently proposed variants of the ABC algorithm and various other available methods for estimating induction motor parameters. The performance of the proposed algorithm is also assessed using the chaotic map initialization technique. The results prove that the proposed algorithm is able to achieve the best convergence among all of the compared algorithms.

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“…Despite their success in system identification, traditional optimization techniques have some fundamental problems including their dependence on unrealistic assumptions such as unimodal performance landscapes and differentiability of the performance function, and trapping in local minima [6].…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification Based on a Modified PSO Applied to Suspension System

Alfi

Fateh²

2010

JSEA

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Parameter identification of induction motors using stochastic optimization algorithms

Cited by 68 publications

References 15 publications

Parameter identification of a cage induction motor using particle swarm optimization

Parameter identification of a cage induction motor using particle swarm optimization

Multiple-global-best guided artificial bee colony algorithm for induction motor parameter estimation

Parameter Identification Based on a Modified PSO Applied to Suspension System

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