Yao Duan

Comparison of Particle Swarm Optimization and Genetic Algorithm in the design of permanent magnet motors

The complexity of the electric machine structure makes an optimal design a difficult and challenging task. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are two popular methods for their advantages such as gradient-free and ability to find global optima. Due to the fact that the machine design models are sometimes computationally intense, it is important for the optimization algorithms used in the design practice to have high computational efficiency. This paper uses the design of a Surface Mount Permanent Magnet (SMPM) machine with an analytical model as a benchmark and compares the performance of PSO and GA in terms of their accuracy, the robustness to population size and algorithm coefficients. The results show that PSO has advantages over GA on those aspects and is preferred over GA when time is a limiting factor. Similarities in the machine design problems make the comparison result also applicable to the design of other types of machines and with other modeling methods

Present and future trends in wind turbine generator designs

Various wind turbine generators have been designed and manufactured in recent years, such as the traditional induction generator, the doubly fed induction generator, the field excited synchronous generator and the permanent magnet synchronous generator. Generators are coupled to the turbine by either a gearbox or without one (so-called direct drive), and there are four different kinds of interfaces between the generator and the grid, depending on the type of power electronic converters used. This paper reviews recent developments in the different generator topologies and their connections to the turbine. A new generator design concept without a gear box or any power electronics is proposed and the feasibility is illustrated in this paper.

A Novel Method for Multiobjective Design and Optimization of Three Phase Induction Machines

A fast and efficient multiobjective optimization design method is developed for induction machines, which requires much fewer design iterations than the traditional design methods. In this new method, the number of prime variables that define the optimization is reduced to only six. A canonical particle swarm optimization (PSO) method with penalty function for design constraints is developed to find the optimal solution for a user-defined objective function. After several trial solutions with the PSO, the optimal regions for both the design variables and the performance indexes can be estimated. The results will provide useful information for both a drive system designer and a machine designer at an early stage of the design process. A comparison study of PSO and genetic algorithm (GA) is also performed in this paper, and the comparison shows that PSO is more successful in finding the global optima and also has better computational efficiency than GA. The original contributions of this paper are a novel induction machine design method, consideration of winding turn selection limitation, and a machine-design-focused comparison.

Optimal Electromagnetic-Thermo-Mechanical Integrated Design Candidate Search and Selection for Surface-Mount Permanent-Magnet Machines Considering Load Profiles

Most existing design and optimization methods treat the electromagnetic, thermal, and mechanical designs separately. As a result, the effects of power supply, machine control, load profile, thermal effects, and materials are not fully integrated and accounted for, which often leads to over- or underdesign. This paper proposes an innovative and computationally efficient approach which integrates the electromagnetic and thermo-mechanical design for surface mount permanent magnet machines. Particle swarm optimization is part of this integrated process to efficiently find design candidates which optimize certain requirements, such as weight, efficiency, etc. The effects of power supplies, machine controls, load profiles, thermal effects, and materials can thus all be considered systematically in the proposed multiphysics design approach. This results in a multiphysics design tool that can rapidly locate an optimal design candidate for further consideration.

Multi-objective design optimization of Surface Mount Permanent Magnet machine with particle swarm intelligence

An efficient multi-objective design method with particle swarm optimization (PSO) is developed for surface mount permanent magnet machines to reduce the complexity in the PMSM machine design. First an analytical model of the PMSM machinepsilas geometry is developed and results are verified by finite element analysis. With proper design specification and assumption, the design input variables in this model can be reduced to as low as two, which significantly simplifies the optimization process. PSO is then applied to this analytical model. Compared to the traditional machine design methods, this proposed algorithm finds the optimized solution with fast computation and high convergence.

A useful multi-objective optimization design method for PM motors considering nonlinear material properties

A fast and efficient multi-objective optimization design method is developed for the Surface Mount Permanent Magnet (SMPM) class of electric machines. An analytical design model of the SMPM machines with considerations of the steels nonlinear property is developed and in this analytical model the number of variables to be optimized is reduced to only three. A canonical Particle Swarm Optimization (PSO) method with penalty function for the designs having magnetic saturation is developed to find the optimal solution for a user defined objective function. After several trial solutions with the PSO, the optimal regions for both the design variables and the performance indexes can be estimated. The results will provide useful information for both the drive system designer and the machine designer at an early stage of the design process.

Design of a 750,000 rpm switched reluctance motor for micro machining

This paper presents a detailed design process of an ultra-high speed, switched reluctance machine for micro machining. The performance goal of the machine is to reach a maximum rotation speed of 750,000 rpm with an output power of 100 W. The design of the rotor involves reducing aerodynamic drag, avoiding mechanical resonance, and mitigating excessive stress. The design of the stator focuses on meeting the torque requirement while minimizing core loss and copper loss. The performance of the machine and the strength of the rotor structure are both verified through finite-element simulations The final design is a 6/4 switched reluctance machine with a 6mm diameter rotor that is wrapped in a carbon fiber sleeve and exhibits 13.6 W of viscous loss. The stator has shoeless poles and exhibits 19.1 W of electromagnetic loss.

A novel method for multi-objective design and optimization of three phase induction machines

A fast and efficient multi-objective optimization design method is developed for induction machines, which requires much fewer design iterations than the traditional design methods. In this new method the number of prime variables that define the optimization is reduced to only six. A canonical Particle Swarm Optimization (PSO) method with penalty function for design constraints is developed to find the optimal solution for a user defined objective function. After several trial solutions with the PSO, the optimal regions for both the design variables and the performance indexes can be estimated. The results will provide useful information for both a drive system designer and a machine designer at an early stage of the design process. A comparison study of PSO and Genetic Algorithm (GA) is also performed in this paper and the comparison shows that PSO is more successful in finding the global optima and also has better computational efficiency than GA.

Method for multi-objective optimized designs of Surface Mount Permanent Magnet motors with concentrated or distributed stator windings

A fast and efficient multi-objective design method is developed for comparison of the traditional Distributed Winding (DW) and the more popular Concentrated Winding (CW) configuration for Surface Mount Permanent Magnet (SMPM) machines. First an analytical design model previously developed for DW machines is adapted for CW machines and the accuracy is verified by Finite Element Analysis (FEA); then the Particle Swarm Optimization (PSO) method is applied to optimize the design. With an objective function defined by the user to emphasize machine performance indexes, such as weight, volume, efficiency and so on, the PSO optimized designs of the two winding type machines provide valuable insight on winding choices. Two example objective functions and their respective optimized designs for both winding types are illustrated and analyzed. Another merit of the proposed method is that it is FEA-free, fast and efficient, which will save significant time and energy for machine designers.

Optimal electromagnetic-thermo-mechanical integrated design for surface mount permanent magnet machines considering load profiles

Most existing design and optimization methods treat the electromagnetic, thermal and mechanical designs separately. As a result, the effects of power supply, machine control, load profile, thermal effects and materials are not fully integrated and accounted for, which often leads to over- or under- design. This paper proposes an innovative and computationally efficient approach which integrates the electromagnetic and thermo-mechanical design for Surface Mount Permanent Magnet (SMPM) machines. Particle Swarm Optimization (PSO) is part of this integrated process to efficiently find designs which optimize certain requirements, such as weight, efficiency, etc. for example. The effects of power supplies, machine controls, load profiles, thermal effects and materials can thus all be considered systematically in the proposed multi-physics design approach.