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

Sakthivel, V.; Ramachandran, B.; Subramanian, S.

doi:10.1016/j.engappai.2009.06.004

Cited by 84 publications

(63 citation statements)

References 27 publications

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“…An induction motor is a highly nonlinear system and the electric variables of the rotor are not measurable [3,6,11]. Additionally, the skin effect and magnetic saturation complicate the modeling process of the machine even more.…”

Section: Problem Formulationmentioning

confidence: 99%

“…The results of the PSO and GA for the parameter estimation of the induction motor were taken from [11]. The data of the induction motor and parameter settings of the PSO and GA can also be seen in [11]. The PSO and GA are run for 200 generations, whereas the proposed EABC algorithm is run for 100 generations, for the same population size.…”

Section: Comparison With the Pso And Ga Algorithmsmentioning

confidence: 99%

“…PSO is a prominent element of the swarm intelligence-based optimization algorithms, whereas GA is an efficient element of the evolutionary optimization algorithms [11]. The results of the PSO and GA for the parameter estimation of the induction motor were taken from [11].…”

Section: Comparison With the Pso And Ga Algorithmsmentioning

confidence: 99%

“…Their work finally concluded that bioinspired optimization algorithms are far superior to conventional iterative methods. Hence, researchers have employed the optimization algorithms to determine the induction motors parameters [6,11] because the algorithms have an inherent capability to find either the optimal solution or very near optimal solutions of the problem at hand [2,12].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Problem Formulationmentioning

confidence: 99%

Section: Comparison With the Pso And Ga Algorithmsmentioning

confidence: 99%

Section: Comparison With the Pso And Ga Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…In this method, each particle tries to match its behavior to the behavior of the group and its own previous behaviors in every repetition. Each particle has d dimensions, which is in fact equal to the variables existing in cost function [14][15][16][17]. 1 2 ( , ,..., )…”

Section: Particle Swarm Optimization Algorithm (Pso)mentioning

confidence: 99%

A New Approach to Improve Induction Motor Performance in Light-Load Conditions

Hesari¹,

Hoseini²

2017

Journal of Electrical Engineering and Technology

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-Induction motors often reach their maximum efficiency at the nominal load. In most applications, the machine load is not equal to the nominal load, thus reduces the motor efficiency and turns a greater percent of power into loss. In this paper, the induction motor control problem has been investigated to reduce the system losses. The Field Oriented Control method (FOC) has been employed in this paper. In this research, the mathematical equations related to system losses are calculated in relation to torque and speed, and then the q-and d-axis are summarized according to the current components. After that, the proposed method is applied along with d-and q-axis. In the recent three decades, many techniques have been suggested to improve the induction motor performance using smart and non-smart methods. In this paper, a new PSO-Fuzzy method have used in real time. The fuzzy logic method serves as speed controller in q-axis and PSO algorithm controls the optimum flux in d-axis. It will be proved that the use of this combined method will lead to a significant improvement in motor efficiency.

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Rao

2010

Springer Series in Advanced Manufacturing

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

Cited by 84 publications

References 27 publications

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

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

A New Approach to Improve Induction Motor Performance in Light-Load Conditions

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