scholarly journals Optimization of double stator PMSM with different slot number in inner and outer stators using genetic algorithm

2021 ◽  
Vol 12 (2) ◽  
pp. 726
Author(s):  
Mohd Saufi Ahmad ◽  
Dahaman Ishak ◽  
Tiang Tow Leong ◽  
Mohd Rezal Mohamed
Keyword(s):  
Genetic Algorithm ◽  
Performance Enhancement ◽  
Optimization Technique ◽  
Analytical Calculation ◽  
Harmonic Distortion ◽  
Torque Ripple ◽  
Development Stage ◽  
Objective Functions ◽  
Algorithm Optimization ◽  
Slot Opening

<span lang="EN-US">This paper describes the performance enhancement of double stator permanent magnet synchronous machines (DS-PMSM) based on genetic algorithm optimization (GAO). Generally, throughout the development stage, an analytical calculation is implemented to build the initial model of the DS-PMSM since the analytical calculation can provide the initial parameters based on the types and materials used in the machine design. For further improvement, GAO might potentially be applied to provide the optimization technique in searching the optimal motor parameters iteratively and intelligently with specific objective functions. For this aim, a three-phase, DS-PMSM with different number of slots between the outer and inner stators is first designed by using analytical parameter estimation and then later optimized by GAO. The outer and inner stators have 12-slots and 9-slots respectively, while, the rotor carries 10 magnetic poles. Four main input motor parameters, i.e. outer stator slot opening, outer magnet pole arc, inner stator slot opening and inner magnet pole arc are varied and optimized to achieve the design objective functions, i.e. high output torque, low torque ripple, low cogging torque and low total harmonic distortion (THDv). The results from the optimized GAO are compared with the initial motor model and further validated by finite element method (FEM). The results show a good agreement between GAO and FEM. GAO has achieved very significant improvements in enhancing the machine performance.</span>

scholarly journals Optimization of the Parameters of RISE Feedback Controller Using Genetic Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Fayiz Abu Khadra ◽  
Jaber Abu Qudeiri ◽  
Mohammed Alkahtani
Keyword(s):  
Genetic Algorithm ◽  
Numerical Simulations ◽  
Control Algorithm ◽  
Chaotic Systems ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Algorithm Optimization ◽  
External Disturbances ◽  
Van Der Pol ◽  
Control Methodology

A control methodology based on a nonlinear control algorithm and optimization technique is presented in this paper. A controller called “the robust integral of the sign of the error” (in short, RISE) is applied to control chaotic systems. The optimum RISE controller parameters are obtained via genetic algorithm optimization techniques. RISE control methodology is implemented on two chaotic systems, namely, the Duffing-Holms and Van der Pol systems. Numerical simulations showed the good performance of the optimized RISE controller in tracking task and its ability to ensure robustness with respect to bounded external disturbances.

Genetic Algorithm optimization of initial blank shape in deep drawing of a stepped work piece

2016 ◽  
Vol 231 (5) ◽  
pp. 1063-1074
Author(s):  
Yasser Rostamiyan ◽  
Mohammad Abbasi
Keyword(s):  
Genetic Algorithm ◽  
Rolling Direction ◽  
Work Piece ◽  
Low Carbon ◽  
Objective Functions ◽  
Algorithm Optimization ◽  
Initial Shape ◽  
Expansion Level ◽  
Blank Shape ◽  
St14 Steel

This study considers the effect of forging direction on the initial shape of sheet to create a stepped work piece. The purpose of this study is to consider rolling direction in 0°, decreasing the waste while producing workpieces and so decreasing total cost of process. To this end, the assumed workpiece was made of a low carbon and anisotropic st14 steel sheet. To find the most appropriate direction and the shortest modification steps for final shape, the expansion level of the sheet was first imaged in the rolling direction and then the piece was shaped by the geometry. This approach was based on the coupling between the simulation and Genetic Algorithm. A Genetic Algorithm based approach is developed to optimize dimensions through integrating a finite element code running to compute the objective functions for each generation. Those points with a few materials modified through Genetic Algorithm yielded better results.

Design Techniques for Reducing Cogging Torque in SPM Motors Using Shifted Slot Opening

2013 ◽  
Vol 284-287 ◽  
pp. 692-696
Author(s):  
Shih Ping Cheng ◽  
Chang Chou Hwang ◽  
Chia Ming Chang
Keyword(s):  
Harmonic Distortion ◽  
Torque Ripple ◽  
Permanent Magnet Synchronous Motors ◽  
Element Analysis ◽  
Cogging Torque ◽  
Synchronous Motors ◽  
Machine Performance ◽  
Slot Opening ◽  
Slot Filling ◽  
Stator Design

Skewing is one of the most common methods to reduce the cogging torque of permanent magnet synchronous motors. This technique may cause manufacturing troubles such as impossible automatic slot filling due to skewing stator slot. One method to overcome this problem is to use the step twisted stator structure with shifted slot opening. Compared to other methods of skewing, in the presented method, the stator slots are still straight slots and the skewing effect is implemented by shifting slot openings coupled with a step twisted stator design. This paper discusses the use of the step twisted stator structure with shifted slot opening and the associated effects on machine performance. We address the cogging torque, average torque, torque ripple, and back-EMF and its total harmonic distortion (THD). We examine the results using finite element analysis (FEA).

scholarly journals Performance Enhancement of the Micromixer by the Multiobjective Genetic Algorithm and Surrogate Model Based on a Navier–Stokes Analysis Using Trade-Off Objective Functions

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shakhawat Hossain ◽  
Farzana Islam ◽  
Nass Toufik Tayeb ◽  
Muhammad Aslam ◽  
Jin-Hyuk Kim
Keyword(s):  
Genetic Algorithm ◽  
Surrogate Model ◽  
Performance Enhancement ◽  
Channel Width ◽  
Navier Stokes ◽  
Design Parameters ◽  
Design Domain ◽  
Objective Functions ◽  
Trade Off ◽  
Multiobjective Genetic Algorithm

Optimal structure of the micromixer with a two-layer serpentine crossing device was accomplished by a multiobjective genetic algorithm and surrogate modeling based on a Navier–Stokes analysis using the trade-off objective functions behavior. The optimization analysis was conducted with three design parameters, i.e., channel width to the pitch span ( w / P ) ratio, major channel width to the pitch span (H/P) ratio, and channel depth to the pitch span (d/P) ratio. Two objective functions (i.e., mixing index and pressure drop) with trade-off characteristics have been used to solve the multiobjective optimization problem. The design domain was predetermined by a parametric investigation; afterward, the Latin hypercube sampling method was employed to select the appropriate design points surrounded by the design domain. The numerical data of the thirty-two design points were used to create the surrogate model; among the different surrogate models, in this study, the Kriging metamodel has been used. The concave pareto-optimal curve signifies the trade-off characteristics linking the objective functions.

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