Optimization algorithms inspired by electromagnetism and stigmergy in electro-technical engineering (original) (raw)
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The efficiency of universal electric motors that are widely used in home appliances can be improved by optimizing the geometry of the rotor and the stator. Expert designers traditionally approach this task by iteratively evaluating candidate designs and improving them according to their experience. However, the existence of reliable numerical simulators and powerful stochastic optimization techniques make it possible to automate the design procedure. We present a comparative study of six stochastic optimization algorithms in designing optimal rotor and stator geometries of a universal electric motor where the primary objective is to minimize the motor power losses. We compare three methods from the domain of evolutionary computation, generational evolutionary algorithm, steady-state evolutionary algorithm and differential evolution, two particle-based methods, particleswarm optimization and electromagnetism-like algorithm, and a recently proposed multilevel ant stigmergy algorithm. By comparing their performance, the most efficient method T. Tušar . B. Filipič ( ) for solving the problem is identified and an explanation of its success is offered.
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This study presents design optimization of permanent magnet synchronous motor by using different artificial intelligence methods. For this purpose, three stochastic optimization methods—genetic algorithm, simulated annealing, and differential evolution—were used. Motor design parameters and efficiency results obtained by the artificial intelligence methods were compared with each other. The results were later checked by finite element analysis. Consequently, the motor efficiencies obtained from the algorithms have high accuracy. Approaches strategies of the artificial intelligence algorithms are quite sufficient and remarkable for design optimization of permanent magnet synchronous motor. The differential evolution is better and more reliable optimization method nevertheless.
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IEEE Transactions on Industrial Electronics, 2003
We present a new design procedure that improves the efficiency of a universal motor, the type of motor that is typically used in home appliances and power tools. The goal of our optimization was to find the independent geometrical parameters of the rotor and the stator with the aim of reducing the motor's power losses, which occur in the iron and the copper. Our procedure is based on a genetic algorithm (GA) and by using this procedure we were able to significantly improve the motor's efficiency-the ratio of the motor's output power to its input power. The GA proved to be a simple and efficient search-and-optimization method for solving this day-to-day design problem in industry. It significantly outperformed a conventional "direct" design procedure that we had used previously.
Design optimization of Electric Machines
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The application of genetic algorithms (GAS) to the design optimization of electromagnetic devices is presented in detail. The method is demonstrated on a magnetizer by optimizing its pole face to obtained the desired magnetic flux density ditribution. The shape of the pole face is constructed from the control points by means of uniform nonrational bsplines.
A Hybrid Optimization Algorithm for Electric Motor Design
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This paper presents a hybrid algorithm employed to reduce the weight of an electric motor, designed for electric vehicle (EV) propulsion. The approach uses a hybridization between Cuckoo Search and CMAES to generate an initial population. Then, the population is transferred to a new procedure which adaptively switches between two search strategies, i.e. one for exploration and one for exploitation. Besides the electric motor optimization, the proposed algorithm performance is also evaluated using the 15 functions of the CEC 2015 competition benchmark. The results reveal that the proposed approach can show a very competitive performance when compared with different state-of-the-art algorithms.
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The "off the shelf" (commercially available) brushless permanent magnet motor is evaluated for electromagnetic losses minimization in order to increase motor efficiency. The proposed analysis is conducted with the perspective of satisfying global thinking approach to the evaluation by "Life Cycle Assessment". This will be done in an attempt to reduce the power consumption impact of the brushless permanent magnet motor in its industrial application.
Evaluating hybrid optimization algorithms for design of a permanent magnet generator
Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) are used to minimize the cost of a permanent magnet (PM) synchronous generator with concentrated windings for tidal power applications. With the use of MATLABs global optimization toolbox, it is possible to run several optimization algorithms on the same problem, and to combine the two stochastic solvers GA and PSO with the gradient based solver fmincon to produce two hybrid optimization solvers. It has been shown that a complex machine design problem with tight constraints and a narrow solution space is difficult to solve for both a GA and for PSO. Both GA and PSO were unable to find the optimal value on their own. Hybrid versions of GA and PSO gave better results. The average minimum costs found with hybrid PSO and hybrid GA were 1.07 and 1.11 times the global minimum. When the integer value was set to the optimal value, the hybrid GA found a mean cost only 1.01 times the global minimum. For both algorithms, it was necessary to increase the population size to improve the fitness functions and reduce the variance.
IEEE Transactions on Industry Applications, 2013
This paper systematically covers the significant developments of the last decade, including surrogate modeling of electrical machines and direct and stochastic search algorithms for both single-and multi-objective design optimization problems. The specific challenges and the dedicated algorithms for electric machine design are discussed, followed by benchmark studies comparing response surface (RS) and differential evolution (DE) algorithms on a permanent-magnet-synchronous-motor design with five independent variables and a strong nonlinear multiobjective Pareto front and on a function with eleven independent variables. The results show that RS and DE are comparable when the optimization employs only a small number of candidate designs and DE performs better when more candidates are considered.
Multi-objective optimal design of permanent magnet synchronous motor
2016 IEEE International Power Electronics and Motion Control Conference (PEMC), 2016
There is a strong demand for the research of electric vehicles (EVs) in automotive industry, because of an increase concern of the energy depletion and environmental pollution problems caused by oil-fueled automotive. The traction motor drive system is one of the core components of EVs. And a motor with superior dynamic performance and high efficiency could significantly reduce energy consumption and improving riding comfort of EVs. Therefore, in order to achieve high dynamic performance and high efficiency of permanent magnet synchronous motor (PMSM), a multi-objective optimization design method for PMSM based on the artificial bee colony (ABC) algorithm was proposed in this paper. First, based on the magnetic field analytical model of PMSM, the analytical expressions of the key parameters were deduced, namely mechanical time constant and electrical time constant. Second, the efficiency, and electrical and mechanical time constant were defined as optimization objectives. Third, the efficiency and dynamic performance of the original motor and optimized motor were compared applying the finite element analysis. Furthermore, one prototype machine was manufactured according to the results of optimization. The dynamic performance and efficiency of the prototype had been tested. The experiments show confident results that the efficiency increased about 1%, the mechanical time constant reduced to 31.4% of initial value.