Artificial immune system for parameter estimation of induction motor

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

doi:10.1016/j.eswa.2010.02.034

Cited by 29 publications

(24 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%

“…Moreover, most of the auxiliaries of thermal power plants are driven by induction motors. To ensure the successful restart of the plants, it is important to investigate the feasibility of the auxiliary motor start-ups [6,8]. Therefore, the exact information of the motor circuit parameters is essential for the design, performance evaluation, and control applications of the motor.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the energy usage assessment of motors is an essential issue in industry for implementing energy-saving strategies. Induction motors are the most widely used motors in industries because of their lower maintenance requirement, lower space requirement, and easy controlling [1,[5][6][7]. Moreover, most of the auxiliaries of thermal power plants are driven by induction motors.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…When the immune system meets a certain antigen at the first time, the system will adjust its composition and structure to identify the antigen better [4,5]. The optimal antibody will be saved as memory after identified [6].…”

Section: Introductionmentioning

confidence: 99%

Research on Image Classification Algorithm Based on Artificial Immune Learning

Zhang

Liang

Wan

et al. 2011

Computer and Computing Technologies in Agriculture IV

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Abstract. On the basis of analyzing immune learning mechanism, by modeling for image classification, we can solve the problem of remote sensing image classification by using the basic principles of the use of immune learning. We have realized a classification algorithm with a function of the immune learning. Classification algorithm divides each major category into a number of small categories and the antigen population evolutionary process of each category is considered separately, therefore the convergence time is greatly decreased. When classifying, we use a variety of different ways to discriminate and introduce artificial priori knowledge to improve the classification accuracy. The results show that the algorithm can be well applied in remote sensing image classification.

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