Efficiency optimization of induction motor drive using genetic algorithms

Rouabah, Z.; Zidani, F.; Abdelhadi, B.

doi:10.1049/cp:20080512

Cited by 4 publications

(2 citation statements)

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“…One problem that existed in LMC was to use differentiation, while the genetic algorithm does not need differentiation and/or any type of auxiliary information, and only the objective function and the fitness determinant style for raw information should be specified for search [9]- [10]. In [11][12][13]16] the genetic algorithm has been used to Minimize Losses. The main approach of this paper is to optimize the flux level and/or d-axis current.…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…There are still ways to complement the future researches in the existing methods investigated in this paper, it is suggested that: Firstly, ANNs are not employed to estimate resistance differences Rd and Rq in motor, which can be used in [11][12][13][14][15][16] in genetic optimization process. Secondly, in [39] the genetic-fuzzy method is employed separately to control q-and daxes.…”

Section: Suggestionsmentioning

confidence: 99%

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Investigating the Intelligent Methods of Loss Minimization in Induction Motors

et al. 2018

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Induction motors are widely used in industry. Given the increasing demand for electric machines in different industries, optimization of these machines to achieve a high efficiency with low cost is of utmost importance. Loss-minimization in motor is done in three ways: 1) optimizing motor selection and design; 2) improving motor power supply waveforms; and 3) using appropriate controlling methods in drives. Often, inductive motors provide the maximum efficiency in their nominal load. In most applications it is necessary for a motor to work in light loads for a long time, e.g. in conveyors, elevators, etc. In these conditions, the machine load is not the nominal load, and a higher percentage of the input power is lost. So, in the case of variable load, the first and second methods cannot increase the efficiency; but the third method provides a large flexibility in decreasing motor losses. In this paper, the application of the third method in lossminimization is reviewed. These motor losses are mostly related to the controlling strategy and basically occur in light-load conditions. There are various strategies to decrease this kind of losses, which are generally divided into two categories: classic methods and intelligent methods. In this paper, first the classic methods, including losses model control (LMC), flux control as a function of torque and search control (SC), are discussed. Then the intelligent methods, such as genetic algorithm, PSO, fuzzy logic and artificial neural network are investigated. This paper is presented while the last methods of efficiency improvement are being investigated and each method is described briefly.

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Section: Genetic Algorithmmentioning

confidence: 99%

Section: Suggestionsmentioning

confidence: 99%

Investigating the Intelligent Methods of Loss Minimization in Induction Motors

et al. 2018

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Dynamic Flux Observation on Variable Parameters in Field Oriented Control for Induction Machine Drives

Grouni

Aibeche

Akroum

2012

Chaos and Complex Systems

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A Novelty Design Of Minimization Of Electrical Losses In A Vector Controlled Induction Machine Drive

Aryza

Irwanto

Lubis

et al. 2018

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract.The induction motor has in the industry . More attention has been a focus to develop and design of induction motor drive. With the method of vector control novelty prove the efficiency of induction motor over their entire speed range. In this paper desirable to design a loss minimization controller which can improve the efficiency. Also, this research described Modeling of an induction motor with core loss included. Realization of methods vector control for an induction motor drive with loss element included. The case of the loss minimization condition. The procedure was successful to calculate the gains of a PI controller. Though the problem of obtaining a robust and sensorless induction motor drive is by no means completely solved, the results obtained as part of this work point in a promising direction. Introduction.IM are critical components in industrial processes. A motor failure may yield an unexpected interruption at the industrial plant, with consequences in costs, product quality, commonly used in adjustable speed drive systems. Induction motors have been widely employed in various industries as actuators or drivers to produce mechanical motions and forces. Since it is estimated that more than 50% of the world electric energy is generated and consumed by electric machines, to improve an efficiency of electric drives are important [1.2]. Induction motors require both full operating range of speed and fast torque response in operational conditions, regardless of load variations. Namely, induction motors have a high efficiency at rated speed and torque.Its efficient control requires a suitable model with accurate parameters, the minimization of the objective function is carried out using the Particle Swarm Optimization. Particle Swarm Optimization (PSO) is an evolutionary algorithm inspired by social interaction. PSO is an evolutionary once technique (a search method based on a natural system) developed by Kennedy and Eberhart. The basic concept of the PSO technique 'lies in accelerating each particle towards its p best and g best locations, with a random weighted acceleration at each time step. PSO has many parameters, and these are described as follow: V max is the maximum allowable velocity of the particles (i.e. in the case where the velocity of the particle exceeds Vmax, then it is limited to Vmax). Particle swarm optimization (PSO) is one of the modern heuristic algorithms [16], [17]. PSO has attracted great attention due to its features of easy implementation, robustness to control parameters and computation efficiency compared with other existing heuristic algorithms, and has been successful. Particle swarm

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Efficiency optimization of induction motor drive using genetic algorithms

Cited by 4 publications

References 0 publications

Investigating the Intelligent Methods of Loss Minimization in Induction Motors

Investigating the Intelligent Methods of Loss Minimization in Induction Motors

Dynamic Flux Observation on Variable Parameters in Field Oriented Control for Induction Machine Drives

A Novelty Design Of Minimization Of Electrical Losses In A Vector Controlled Induction Machine Drive

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