An intelligent design optimization of a permanent magnet synchronous motor by artificial bee colony algorithm

Abstract

The artificial bee colony algorithm is one of the latest stochastic methods based on swarm intelligence. The algorithm simulates the foraging behavior of honeybees. The structure of the algorithm is quite simple and its coding is very easy. This paper proposes a design optimization based on geometrical variables to obtain a highly efficient surfacemounted permanent magnet synchronous motor with concentrated winding by use of the artificial bee colony algorithm.Input parameters for the algorithm are the geometrical variables of the motor. This approach is more advantageous than finite element analysis requiring a long period of time. Results of the artificial bee colony algorithm are compared with results of a genetic algorithm and checked with a commercial design program. The results emphasize the effectiveness of the algorithm on the design optimization of the permanent magnet synchronous motor.

Gri Kurt Optimizasyon Algoritmasını Kullanarak PM Senkron Motorun Tasarım Optimizasyonu

“…Thermal and mechanical conditions regarding surface mounted PMSM design are considered ideal. Some important design equations are as follows [Mutluer and Bilgin, 2016]:…”

“…where, indicates Steinmetz constant, shows electrical angular velocity, ℎ and are iron losses coefficients as hysteresis and as eddy current, out is power of the motor, is copper loss, is iron loss, and is motor efficiency. Other intermediate equations and parameters are given in [Mutluer and Bilgin, 2016;Hanselman, 1994;Pyrhonen et al, 2008].…”

An approach based on non-dominated sorting genetic algorithm III for design of permanent magnet synchronous motor

“…In this study, it was aimed to increase efficiency and reduce cost. Obtaining all PMSM design equations is a very detailed process and therefore references have been made to different studies [1], [7], [16][17]. As a result, the objective functions are obtained as follows.…”

“…So far, many evolutionary algorithms have been used in the design optimizations of PMSMs and one of them is undoubtedly the genetic algorithm (GA). GA is generally used in single objectives to increase efficiency, reduce weight, and eliminate harmonics [5][6][7]. In fact, optimizations in engineering problems such as PMSM design often depend on multi-objective and the objectives often conflict with each other, so single objective algorithms cannot solve these problems at the desired level.…”

A fuzzy-genetic based design of permanent magnet synchronous motor

“…A suitable association for all objectives was aimed, namely to increase the motor efficiency, to reduce the motor weight and to reduce the weight of the magnets. The acquisition of these functions is a very detailed process and therefore references to different studies have been made [1,2,[16][17][18][19]. As a result, the objective functions required for this study were obtained in the following order, motor efficiency, motor weight and weight of magnets.…”