Multiobjective optimal design of saturated surface‐mounted permanent magnet synchronous motors

Ilka, Reza; Alinejad‐Beromi, Yousef

doi:10.1002/jnm.2592

Cited by 5 publications

(4 citation statements)

References 25 publications

(32 reference statements)

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“…Yousef [40] employ k-means clustering strategy to select the final solutions from the optimal Pareto front. To improve the drawback of low diversity of crowding distance in NSGA-II, Zhang et al [41] proposed an improved k-means cluster to coordinate with crowding distance sorting.…”

Section: Related Workmentioning

confidence: 99%

Nondominated sorting genetic algorithm II with merged strategies for industrial network topology optimization

Wang

2020

Concurrency and Computation

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Summary Fast nondominated sorting genetic algorithm II (NSGA‐II) is a popular multiobjective optimization method. However, the tournament selection strategy for crossover operator suffers from the drawback of repetitively selecting the same individuals, resulting in unsatisfying performance. To alleviate this problem, this article first proposes to employ k‐means clustering strategy to divide the candidate individuals into multiple clusters. After that, the crossover operator are redefined with three crossover individuals, where the first and second individuals are forced to selected from the same cluster and the second and third ones are from different clusters. The newly proposed crossover operator is not only able to alleviate the phenomenon above, but also able to retain the advantage of the original tournament selection strategy. The proposed method is verified on two popular test suits, including DTLZ and ZDT test suit and an industrial network topology optimization problem. Experimental results demonstrate that the proposed method exhibits excellent performance on both the two test suits and the practical network topology optimization.

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Section: Related Workmentioning

confidence: 99%

Nondominated sorting genetic algorithm II with merged strategies for industrial network topology optimization

Wang

2020

Concurrency and Computation

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“…Permanent‐magnet synchronous motors are currently used in a wide variety of engineering applications like electric vehicles, hybrid electric vehicles, robotics, computer numerical control (CNC) machines, aviation, aerospace, marine explorer, rail traction, pumps, fans, elevators, compressors, and home appliances (see and references therein). Therefore, remarkable relevance of high‐performance motion control of the synchronous motor can be immediately inferred.…”

Section: Introductionmentioning

confidence: 99%

“…,2 , values specified for controlled output variables; u q , u d , control input values at the equilibrium point; A CL , closed-loop linear system matrix; k iP , k iI , proportional and integral control gains.…”

mentioning

confidence: 99%

Dynamic output feedback control for desired motion tracking on synchronous motors

Beltrán-Carbajal

Favela‐Contreras

Hernández-Ávila

et al. 2020

Int Trans Electr Energ Syst

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Summary A new dynamic output feedback control scheme for planned motion reference tracking on nonlinear permanent‐magnet synchronous motors in presence of uncertainty is proposed. Parametric uncertainty, unmodeled dynamics, and variable mechanical load torque are considered as time‐varying unknown signals to be suppressed actively by control voltage inputs. Dynamic feedback of output signals to be controlled are only required to perform entire planned motion tracking. The control scheme can be performed on both classes of interior‐ and surface‐permanent‐magnet synchronous motors. Analytical and numerical results prove the effectiveness of the dynamic output feedback control method on multi‐input multi‐output nonlinear synchronous motors.

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“…Regarding the optimization problems of SMPMSM, researchers have made a lot of achievements: A Sarikhani [10] optimized the cost and torque pulsation of SMPMSM by combining a motor physical model with the genetic-particle swarm algorithm. R Ilka [11] studied the volume and efficiency issues of SMPMSM, and according to the k-means algorithm, he classified the Pareto-front obtained from the NSGA-II algorithm to assist designers in choosing the best solution. B. Xiaohua [12] derived the expression formula between SMPMSM cogging torque and permanent magnet size on the basis of energy method and Fourier decomposition method, and further optimized the permanent magnet size to reduce the motor cogging torque.…”

Section: Introductionmentioning

confidence: 99%

Power Density Analysis and Optimization of SMPMSM Based on FEM, DE Algorithm and Response Surface Methodology

et al. 2019

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Surface-mounted permanent magnet synchronous motors (SMPMSM) with high power density and good speed regulation are widely used in industrial applications. In order to further improve its power density, this paper studied the relationship between the thickness of the stator yoke, the thickness of the rotor yoke, the relative magnet span of the motor and the motor power density using the finite element simulation method. On this basis, a response surface model between the three parameters and the power density of the motor was established. Based on this model and a differential evolution algorithm, the motor was optimized and the power density was improve; finally, the optimization results were verified using the finite element simulation method. In addition, the optimization results showed that, when other structure parameters remain unchanged, there is an optimal combination of parameters that can maximize the motor’s power density, including the thickness of the stator yoke, the thickness of the rotor yoke and relative magnet span of the motor.

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Multiobjective optimal design of saturated surface‐mounted permanent magnet synchronous motors

Cited by 5 publications

References 25 publications

Nondominated sorting genetic algorithm II with merged strategies for industrial network topology optimization

Nondominated sorting genetic algorithm II with merged strategies for industrial network topology optimization

Dynamic output feedback control for desired motion tracking on synchronous motors

Power Density Analysis and Optimization of SMPMSM Based on FEM, DE Algorithm and Response Surface Methodology

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