Improved Fuzzy-Based Taguchi Method for Multi-Objective Optimization of Direct-Drive Permanent Magnet Synchronous Motors

Youquan, Guo; Si, Jikai; Gao, Caixia; Feng, Haichao; Gan, Chun

doi:10.1109/tmag.2019.2897867

Cited by 41 publications

(19 citation statements)

References 9 publications

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“…This means that in each pair of columns, all factor combinations occur the same number of times. Orthogonal designs estimate the effect of each factor on the objectives independently of all other factors 33 . The number of variables in this article is eight.…”

Section: Sensitivity Analysis Based On Doementioning

confidence: 99%

Sensitivity analysis and multiobjective design optimization of flux switching permanent magnet motor using MLP‐ANN modeling and NSGA‐II algorithm

Mahmouditabar

Vahedi

Mosavi

et al. 2020

Int Trans Electr Energ Syst

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Summary The flux switching permanent magnet (FSPM) motor is relatively a new topology of the permanent magnet (PM) motors, which both PM and armature winding are placed at the stator. This feature leads to more robust design, better heat dissipation, and a proper option for a wide range of industrial applications. The critical part of the development of FSPM motor is the design optimization of the structure to improve the electromagnetic performance of the motor. In this article, first to reduce the computation time and required memory of the optimization procedure, the multiobjective sensitivity analysis based on design of experiment is performed to specify the most effective parameters on the objectives. Then, the initial samples data of the optimization procedure is obtained by 2D finite element method (FEM) model of the FSPM motor, which is validated by the prototype of the motor. Furthermore, based on FEM results the multilayers perceptron artificial neural network for the approximation of relation between design variables and objectives is implemented. Finally, using the nondominated sorting genetic algorithm‐II, the optimization procedure of the FSPM motor is done. The accuracy of the presented optimization procedure is validated by a comparison of the initial prototype and final design of the motor.

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Section: Sensitivity Analysis Based On Doementioning

confidence: 99%

Sensitivity analysis and multiobjective design optimization of flux switching permanent magnet motor using MLP‐ANN modeling and NSGA‐II algorithm

Mahmouditabar

Vahedi

Mosavi

et al. 2020

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…If 3 three-level factors are considered, the number of full-factorial experiments is 9, and the number of Box Behnken design is 15. Latin Hypercube Sampling method belongs to the stratified sampling [23], which number depends on the selection of interval. Therefore, these methods are often costly and prohibitive, especially when applied industrially.…”

Section: Orthogonal Arraymentioning

confidence: 99%

Design Synchronous Generator Using Taguchi-Based Multi-Objective Optimization

Cheng

Hong

et al. 2020

Energies

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The extensive use of finite element models accurately simulates the temperature distribution of electrical machines. The simulation model can be quickly modified to reflect changes in design. However, the long runtime of the simulation prevents any direct application of the optimization algorithm. In this paper, research focused on improving efficiency with which expensive analysis (finite element method) is used in generator temperature distribution. A novel surrogate model based optimization method is presented. First, the Taguchi orthogonal array relates a series of stator geometric parameters as input and the temperatures of a generator as output by sampling the design decision space. A number of stator temperature designs were generated and analyzed using 3-D multi-physical field collaborative finite element model. A suitable shallow neural network was then selected and fitted to the available data to obtain a continuous optimization objective function. The accuracy of the function was verified using randomly generated geometric parameters to the extent that they were feasible. Finally, a multi-objective genetic optimization algorithm was applied in the function to reduce the average and maximum temperature of the machine simultaneously. As a result, when the Pareto front was compared with the initial data, these temperatures showed a significant decrease.

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“…Compared with the conventional proportional integral derivative (PID) control, model predictive current control (MPCC) has faster dynamic response, a lower current ratio, and fewer parameter adjustments, since no PID regulator is adopted [19,20]. Recently, artificial intelligence (AI) techniques, such as the artificial ant clustering technique, neural network algorithm, and fuzzy control method, have attracted more attention and have been successfully applied in fault diagnosis and motor modeling [21][22][23]. However, AI techniques have not been widely applied in motor drives, since they have heavy computation burden compared with MPC.…”

Section: Introductionmentioning

confidence: 99%

“…So far, various MPC formulations have been proposed for PMSM drives, and they can be mainly categorized into model predictive torque control (MPTC) [22] and model predictive current control (MPCC) [23]. In MPTC, torque and stator flux are selected as the control variables, and their errors are combined together to form a cost function.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Model Predictive Current-Controlled Permanent Magnet Synchronous Motor Drives with Inaccurate DC Bus Voltage Measurement

Wang

2020

Energies

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In this paper, the effects of inaccurate DC bus voltage measurement are analyzed to model predictive current-controlled permanent magnet synchronous motor (PMSM) drives. It is found that the selection of the optimal space vector is affected by inaccurate DC bus voltage measurements, and the shortest distance principle is proposed to evaluate the effects. With the under-voltage measurement, the actual q axis current is always larger than the reference value, and PMSM may be damaged by the over-current phenomenon. However, the actual q axis current is always smaller than the reference value with the over-voltage measurement, and the rated torque capacity cannot be properly used. The effects of the over-voltage measurement are more serious than those of the under-voltage measurement. Additionally, the larger DC bus voltage measurement error can result in more serious effects than with the over-voltage measurement. Considering the limited variation range of the actual DC bus voltage, the rated value can be set as the measured value and the effects can be neglected. However, the effects should be taken into account if the variation range of the actual DC bus voltage is large. All the theoretical analyses are verified by experimental results.

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Improved Fuzzy-Based Taguchi Method for Multi-Objective Optimization of Direct-Drive Permanent Magnet Synchronous Motors

Cited by 41 publications

References 9 publications

Sensitivity analysis and multiobjective design optimization of flux switching permanent magnet motor using MLP‐ANN modeling and NSGA‐II algorithm

Sensitivity analysis and multiobjective design optimization of flux switching permanent magnet motor using MLP‐ANN modeling and NSGA‐II algorithm

Design Synchronous Generator Using Taguchi-Based Multi-Objective Optimization

Analysis of Model Predictive Current-Controlled Permanent Magnet Synchronous Motor Drives with Inaccurate DC Bus Voltage Measurement

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