A New Approach Design Optimizer of Induction Motor using Particle Swarm Algorithm (original) (raw)
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Advanced Particle Swarm Optimization Used for Optimal Design of Single-Phase Induction Motor
2011
The main goal of optimal design of single phase induction motors with permanent capacitor is maximization of the efficiency and minimizing the manufacturing cost. Mathematical classic methods can be used for design of these motors but they need to linearization and simplification in model and formulas. This linearization is caused that design precision decreases while random search methods such as genetic algorithm (GA) and advanced particle swarm optimization (APSO) do not need to model linearization. Regarding the fact that random search methods can be used for design and optimization processes with relative high precision, in this study, APSO algorithm is used for designing single-phase induction motor with permanent capacitor. The objective function is motor efficiency. The results evaluation reveals that the motor design by APSO algorithm is caused that the efficiency increases in comparison with classic methods and GA.
Optimal design of single‐phase induction motor using particle swarm optimization
COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, 2007
PurposeThis paper aims to employ particle swarm optimization (PSO) technique for optimum design of single‐phase induction motor (SPIM) on the basis of maximizing the efficiency of the motor simultaneously satisfying a set of performance constraints.Design/methodology/approachThe design problem of a SPIM is presented as a nonlinear optimization problem on the basis of maximizing the efficiency of the motor. A set of performance constraints are imposed in the optimization procedure. Particle swarm optimization technique is used as an optimization tool for obtaining the motor dimensions corresponding to maximum efficiency. Incorporation of PSO as a derivative free optimization technique in solving SPIM optimum design problem significantly relieves the assumptions imposed on the optimized objective function.FindingsThis approach has been applied to two sample motors and the results are compared with the evolutionary programming (EP) results. It is observed that the proposed approach is ...
Journal of Engineering, 2014
A cost effective off-line method for equivalent circuit parameter estimation of an induction motor using hybrid of genetic algorithm and particle swarm optimization (HGAPSO) is proposed. The HGAPSO inherits the advantages of both genetic algorithm (GA) and particle swarm optimization (PSO). The parameter estimation methodology describes a method for estimating the steady-state equivalent circuit parameters from the motor performance characteristics, which is normally available from the nameplate data or experimental tests. In this paper, the problem formulation uses the starting torque, the full load torque, the maximum torque, and the full load power factor which are normally available from the manufacturer data. The proposed method is used to estimate the stator and rotor resistances, the stator and rotor leakage reactances, and the magnetizing reactance in the steady-state equivalent circuit. The optimization problem is formulated to minimize an objective function containing the ...
International Energy Journal, 2020
In this paper, the on-service non-invasive efficiency estimation using equivalent circuit (EC) for three-phase induction motor replacement program is presented and investigated. The motor equivalent circuit parameters (ECPs) are estimated by particle swarm optimization (PSO) using measurement data during on-service condition. Then, the non-invasive equivalent circuit method (NIECM) for motor efficiency estimation can be performed using the PSO based motor ECPs estimation. In the proposed NIECM, the induction motor ECPs are estimated by using the measured motor voltage, current, real and reactive powers, power factor, and speed. Therefore, the motor efficiency can be non-invasively analyzed. The developed NIECM software has been tested with nine motors in the laboratory and investigated with five motor replacement programs. The experimentation results of the proposed NIECM, comparing to conventional slip method (SM) and current method (CM), are illustrated and discussed. Among NIECM,...
Efficiency and Cost Optimized Design of an Induction Motor Using Genetic Algorithm
IEEE Transactions on Industrial Electronics, 2017
In the context of electricity shortage and an attempt to save the environment, introduction of energy efficient motors in different fields of application have become a necessity. This paves the way for fusing the conventional machine design procedures with optimization techniques. Unfortunately, the mathematics of electrical machine design involves calculations with highly non-linear equation sets, and hence the conventional analytical optimization techniques do not fit well. In this study, design of an efficiency-optimized squirrel cage induction motor is considered, where Genetic Algorithm is chosen as the tool for optimization. The various constraints considered are selected on the basis of material, mechanical and performance considerations as approved by standards and practices. The influence of change of materials and change of upper limit of customer's budget on different key motor design performance indicators are studied with and without cost constraints. Also, a systematic and statistics-based approach is proposed to achieve an optimized motor design, even at very low cost, provided relaxation of some constraints are allowed by the specific application. The optimized results are validated through tests on laboratory prototypes.
The attribute of an induction motor vary with the number of parameters and the performance relationship between the parameters also is implicit. In this paper a multiobjective problem is considered in which three phase squirrel cage induction motor (SCIM) has been designed in such a way that the efficiency is maximized while power density to be minimized simultaneously keeping various constraints in mind. Three well-known single objective methods such as tabu search (TS), simulated annealing (SA) and Genetic algorithm (GA) for comparing Pareto solutions has also been applied. Performance comparison carried out in this paper by performing different numerical experiments. The result shows that NSGA-II outperforms other three for the considered test cases.
COST AND PERFORMANCE OPTIMIZATION OF INDUCTION MOTOR USING GENETIC
This paper presents three different optimal designs of induction motor. The optimally designed motor is compared with classically designed motor, having same ratings. Genetic Algorithm is used for optimization and three objective functions namely efficiency, torque and cost are considered. The motor design procedure consists of a system of non-linear equations, which gives induction motor characteristics, motor performance, magnetic stresses and thermal limits. Genetic Algorithms (GAs) give satisfactory results in the design optimization of electrical machinery, it has been observed that the GAs locate the global optimum region faster than the conventional direct search optimization techniques. Nowadays optimization of induction machine is making trade-off between different objectives such as a particular item of performance, cost of machine or quality or reliability.
Optimal design of induction motor for a spinning machine using population based metaheuristics
2010
Abstract—This paper deals with the design optimization of a squirrel-cage three-phase induction motor, selected as the driving power of spinning machine in textile industry, using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Efficiency, which decides the operating or running cost of the motor (industry), is considered as objective function. First, the algorithms are applied to design a general purpose motor with seven variables and nine performance related parameters with their nominal values as constraints.
Optimization of 20kVA, 3-Phase Induction Motor using Genetic Algorithm
This work optimizes the copper and iron losses in a 20kVA, 4 Pole, 3-phase, 50Hz squirrel cage inductor motor using genetic algorithm. Losses optimization selects the optimal values of the design variables which gives the least losses. Ten design variables were used in optimization process. The optimization was implemented using MATLAB software. The result shows that using the analytical method (without optimization), the losses was 710 W. But with the use of genetic algorithm to optimize the design, the losses were reduced to 642W. A comparison of these two methods shows a 9.6% decrease in losses with the use of optimization, resulting into an increase in efficiency.