Optimal design of single‐phase induction motor using particle swarm optimization (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 Mpso and Fem
2014
In this paper, a new approach is proposed for the optimum design of single-phase induction motor. By using the classical design equations and the evolutionary algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Modified Particle Swarm Optimization (MPSO), a Single Phase Induction Motor (SPIM) was designed with the maximum efficiency. The Finite Element Method (FEM) was used to achieve an accurate model of the motor. This model was used to validate the optimum design instead of implementing it practically that would be expensive. Results show that the efficiency of the motor designed by MPSO is higher compared to the ones designed by other methods. So this algorithm can be proposed as an appropriate tool in design of single-phase induction motors.
A New Approach Design Optimizer of Induction Motor using Particle Swarm Algorithm
2014
First of all, this paper discusses the use of a novel approach optimization procedure to determine the design of three phase electrical motors. The new lies in combining a motor design program and employing a particle-swarm-optimization (PSO) technique to achieve the maximum of objective function such as the motor efficiency. A method for evaluating the efficiency of induction motor is tested and applied on 1.1 kW experimental machines; the aforementioned is called statistics method (SM) and based on the analysis of the influence losses. As the equations which calculate the iron losses make call to magnetic induction. From this point, the paper proposes to evaluate the B(H) characteristic by estimating the circuit’s flux and the counting of excitation. Next, the optimal designs are analyzed and compared with results of another method which is genetic algorithms (GAs) optimisation technique, was done to demonstrate the validity of the proposed method.
Optimum Design of Single Linear Induction Motors by Particle Swarm Optimization
In this Paper, the analysis of the dynamic response of a Linear Induction Motor as an electromechanical system is done, accounting for all the governing equations implied in the process which are used to develop the corresponding simulation models. Once this model is presented, a feedback control system is implemented in order to analyze the controlled response of the motor, considering the applications and conditions analogue to aircraft launcher systems. This procedure will be done by analytic method and by using the particle swarm optimization technique with PI controller. Results will show the accuracy of the equivalent circuit model and the improvement of objective functions at the end of the optimization procedure.
Optimal design of single phase permanent magnet brushless DC motor using particle swarm optimisation
COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, 2014
Purpose-The purpose of this paper is to perform an optimal design of single-phase permanent magnet brushless DC motor (SPBLDCM) using efficiency of the motor as objective function. In the design procedure performed on SPBLDCM, particle swarm optimisation (PSO) as an optimisation tool is used. Design/methodology/approach-The created computer programme for optimal design of electrical machines is based on the particle swarm optimisation. According to the design characteristics of SPBLDCM, some of the motor parameters are chosen to be constant and others variable. A comparative analysis of both motor models based on the value of the objective function, as well as the values of the optimisation parameters, is performed. Findings-From the comparative data analysis of both motor models, it can be concluded that the main objective of the optimisation is realised, and it is achieved by an improvement of the efficiency of the motor. Originality/value-An optimisation technique based on PSO has been developed and applied to the design of SPBLDCM. According to the results it can be concluded that the PSO is a very suitable tool for design optimisation of SPBLDCM and electromagnetic devices in general. The quality of the PSO model has been proved through the data analysis of the prototype and optimised solution. At the end, the quality of the PSO solution has bee again proved by comparative analysis of the two motor models using FEM as a performance analysis tool.
Optimum Design of Single-Sided Linear Induction Motors using Particle Swarm Optimization
– In this Paper, by using a mathematical model, the analysis of the dynamic response of a linear induction motor as an electromechanical system is done. Employing the presented model, a feedback control system is proposed to analyze the controlled response of the motor. This procedure is done by analytical method and PI controller which the coefficients of the latter are set by using the particle swarm optimization technique. The results show the accuracy of the model and the improvement of the objective functions at the end of the optimization procedure.
2010
The paper presents a novel self adjusting wind energy utilization scheme using a modified single phase operation of the three phase induction generator supplemented by a voltage stabilization switched filter compensation scheme. The paper presents a family of novel switched smart filter compensated devices using Green Plug/Energy Management/Energy Economizer GP-EM-EE devices for small single phase induction motors used in residential / commercial motor drives. The GPEM-EE devices are equipped with a dynamic online error driven optimally tuned controller. The scheme is suitable for small scale applications of wind energy utilization in the range from 5-25 KVA.
Synchronous Motor Design using Particle Swarm Optimization Technique
2010
This paper investigates an optimization procedure for the design of a synchronous motor (SM) using a particle swarm optimization (PSO) procedure. The PSO is proposed to minimize the motor volume and to maximize the motor output power. The proposed procedure has two stages for motor design. In the first stage, the stator parameters are optimized while in the second stage, the field and damper winding are designed. The proposed algorithm is efficiently compared with the practical experience-based method. The proposed procedure is efficiently design the SM based on the 4pole prototype synchronous machine which is produced at 27 military production factories. The proposed procedure leads to more economical motor compared to the practical experience based method. Also, the proposed procedure maximizes the SM developed apparent power and reduces the field/damper windings in terms of their conductors' diameters and number of conductor per slot.
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.