Optimization of Magnetic Pole Geometry for Field Harmonic Control in Electric Motors

1994 ◽  
Vol 116 (2) ◽  
pp. 173-178 ◽  
Author(s):  
B. S. Rahman ◽  
D. K. Lieu
Keyword(s):  
Optimization Algorithm ◽  
Permanent Magnets ◽  
Optimization Problems ◽  
Permanent Magnet Motors ◽  
Combinatorial Optimization Problems ◽  
Harmonic Control ◽  
Forcing Function ◽  
Resonant Modes ◽  
Modes Of Vibration ◽  
The Cost

A principal source of vibration in permanent magnet motors and generators is the induced stress from the rotating permanent magnets. The harmonic content of this forcing function may excite resonant modes of vibration in the motor or surrounding structure. Thus attenuation of specific harmonics is of considerable interest. This paper describes a method for optimal shaping of the permanent magnets to eliminate one or more of these harmonics. The analytical model for an optimized 4-pole motor consisted of segmented PMs and a solid ring stator. The permanent magnets were modeled as a number of thin radially cut annular layers with specific sector angles. Changing the shape of the PMs resulted in a different flux density field and thus a different frequency spectrum of the forcing function. Attenuation of specified higher harmonics could be achieved at the expense of increasing other harmonics. For a 4-pole motor, the optimization algorithm was fairly successful at eliminating any one of the 8th, 12th or 16th harmonics. The algorithm used was developed to solve combinatorial optimization problems, and drew heavily upon principles from statistical mechanics. The final pole geometry is dependent upon the choice of the cost function used in the optimization algorithm.

The Origin of Permanent Magnet Induced Vibration in Electric Machines

1991 ◽  
Vol 113 (4) ◽  
pp. 476-481 ◽  
Author(s):  
B. S. Rahman ◽  
D. K. Lieu
Keyword(s):  
Permanent Magnet ◽  
Motor Performance ◽  
Permanent Magnets ◽  
Electric Machines ◽  
Permanent Magnet Motors ◽  
Forcing Function ◽  
Surrounding Structure ◽  
Resonant Modes ◽  
Principal Source ◽  
The Magnetic Field

A principal source of vibration in permanent magnet motors and generators is the induced travelling forces from the rotating permanent magnets acting on the stator. The form of the magnetic field and resulting forcing function in the airgap of such machines is critical. The stator is modelled as a solid ring, with no teeth. Various motor parameters were investigated, including the effects of radial versus parallel magnetization, magnetization tolerances, and radial offset. The results were determined with analytical and FEM models. It was concluded that radial magnetization of the permanent magnets was preferable for both vibration and motor performance. Magnetization tolerances and radial offsets yielded a relatively more populated frequency spectrum for the forcing function and thus could lead to a greater probability of resonant modes being excited in the surrounding structure.

scholarly journals Antlion Algorithm Based on Immune Cloning and Its Truss Optimization

2021 ◽  
Author(s):  
Liyancang Li ◽  
Wuyue Yue Wu
Keyword(s):  
Optimization Algorithm ◽  
Learning Strategy ◽  
Optimization Problems ◽  
Early Stage ◽  
Truss Optimization ◽  
Test Functions ◽  
Combinatorial Optimization Problems ◽  
Fitness Value ◽  
Cauchy Mutation ◽  
Antlion Optimization

Abstract Antlion optimization algorithm has good search and development capabilities, but the influence weight of elite ant lions is reduced in the later stage of optimization, which leads to slower algorithm convergence and easy to fall into local optimization. For this purpose, an antlion optimization algorithm based on immune cloning was proposed. In the early stage, the reverse learning strategy was used to initialize the ant population. The Cauchy mutation operator was added to the elite antlion update to improve the later development ability of the algorithm; finally, the antlion was cloned and mutated with the immune clone selection algorithm to change the position and fitness value of the antlion, and further improve the algorithm's global optimization ability and convergence accuracy. 10 test functions and a 0~1 backpack were used to evaluate the optimization ability of the algorithm and applied to the size and layout optimization problems of the truss structure. The optimization effect was found to be good through the force effect diagram. It is verified that ICALO is applied to combinatorial optimization problems with faster convergence speed and higher accuracy. It provides a new method for structural optimization.This article is submitted as original content. The authors declare that they have no competing interests.

Single Machine Scheduling with Rejection

2012 ◽  
Vol 3 (2) ◽  
pp. 42-61 ◽  
Author(s):  
Atefeh Moghaddam ◽  
Lionel Amodeo ◽  
Farouk Yalaoui ◽  
Behrooz Karimi
Keyword(s):  
Single Machine ◽  
Optimization Problems ◽  
Machine Scheduling ◽  
Single Machine Scheduling ◽  
Phase Method ◽  
Two Phase ◽  
General Technique ◽  
Combinatorial Optimization Problems ◽  
Traditional Research ◽  
The Cost

In this paper, the authors consider a single machine scheduling problem with rejection. In traditional research, it is assumed all jobs must be processed. However, in the real-world situation, certain jobs can be rejected. In this study, the jobs can be either accepted and scheduled or be rejected at the cost of a penalty. Two objective functions are considered simultaneously: (1) minimization of the sum of weighted completion times for the accepted jobs, and (2) minimization of the sum of penalties for the rejected jobs. The authors apply two-phase method (TPM), which is a general technique to solve bi-objective combinatorial optimization problems, to find all supported and non-supported solutions for small-sized problems. The authors present a mathematical model for implementing both phases. On the other hand, three different bi-objective simulated annealing algorithms have also been developed to find a good estimation of Pareto-optimal solutions for large-sized problems. Finally the authors discuss the results obtained from each of these algorithms.

scholarly journals Enhanced Hyper-Cube Framework Ant Colony Optimization for Combinatorial Optimization Problems

Algorithms ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 286
Author(s):  
Ali Ahmid ◽  
Thien-My Dao ◽  
Ngan Van Le
Keyword(s):  
Combinatorial Optimization ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Real Life ◽  
Ant Colony ◽  
Combinatorial Optimization Problems ◽  
The Right ◽  
Design Cost ◽  
Laminated Structures

Solving of combinatorial optimization problems is a common practice in real-life engineering applications. Trusses, cranes, and composite laminated structures are some good examples that fall under this category of optimization problems. Those examples have a common feature of discrete design domain that turn them into a set of NP-hard optimization problems. Determining the right optimization algorithm for such problems is a precious point that tends to impact the overall cost of the design process. Furthermore, reinforcing the performance of a prospective optimization algorithm reduces the design cost. In the current study, a comprehensive assessment criterion has been developed to assess the performance of meta-heuristic (MH) solutions in the domain of structural design. Thereafter, the proposed criterion was employed to compare five different variants of Ant Colony Optimization (ACO). It was done by using a well-known structural optimization problem of laminate Stacking Sequence Design (SSD). The initial results of the comparison study reveal that the Hyper-Cube Framework (HCF) ACO variant outperforms the others. Consequently, an investigation of further improvement led to introducing an enhanced version of HCFACO (or EHCFACO). Eventually, the performance assessment of the EHCFACO variant showed that the average practical reliability became more than twice that of the standard ACO, and the normalized price decreased more to hold at 28.92 instead of 51.17.

scholarly journals Efficient partition of integer optimization problems with one-hot encoding

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Shuntaro Okada ◽  
Masayuki Ohzeki ◽  
Shinichiro Taguchi
Keyword(s):  
Optimization Problems ◽  
Search Space ◽  
Integer Optimization ◽  
Combinatorial Optimization Problems ◽  
Integer Variables ◽  
Partitioning Methods ◽  
Integer Problem ◽  
Feasible Solutions ◽  
The Cost ◽  
D Wave

Abstract Quantum annealing is a heuristic algorithm for solving combinatorial optimization problems, and hardware for implementing this algorithm has been developed by D-Wave Systems Inc. The current version of the D-Wave quantum annealer can solve unconstrained binary optimization problems with a limited number of binary variables. However, the cost functions of several practical problems are defined by a large number of integer variables. To solve these problems using the quantum annealer, integer variables are generally binarized with one-hot encoding, and the binarized problem is partitioned into small subproblems. However, the entire search space of the binarized problem is considerably larger than that of the original integer problem and is dominated by infeasible solutions. Therefore, to efficiently solve large optimization problems with one-hot encoding, partitioning methods that extract subproblems with as many feasible solutions as possible are required. In this study, we propose two partitioning methods and demonstrate that they result in improved solutions.

Sign in / Sign up

Export Citation Format

Share Document