Optimal design of permanent magnet flux switching generator for wind applications via artificial neural network and multi-objective particle swarm optimization hybrid approach

Meo, Santolo; Zohoori, Alireza; Vahedi, A.

doi:10.1016/j.enconman.2015.11.062

Cited by 38 publications

(17 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the purpose of reducing the costs and weight of the machine while maximizing the amplitude of the induced voltage as well as minimizing its total harmonic distortion Meo et al (2016) [100] has presented a new hybrid approach for the design optimization of a direct-drive permanent magnet flux switching generators. Figure 17 presents the results of hybrid model, non-dominated sorting genetic algorithm (NSGA-II), abyss and proposed ANN-multi-objective particle swarm optimization (SMPSO) in terms of cost (a), total harmonic distortions of voltage (THD) (b), weight (c) and rated line voltage; em (d).…”

Section: Hybrid Soft Computing Methodsmentioning

confidence: 99%

Review of Soft Computing Models in Design and Control of Rotating Electrical Machines

et al. 2019

View full text Add to dashboard Cite

Rotating electrical machines are electromechanical energy converters with a fundamental impact on the production and conversion of energy. Novelty and advancement in the control and high-performance design of these machines are of interest in energy management. Soft computing methods are known as the essential tools that significantly improve the performance of rotating electrical machines in both aspects of control and design. From this perspective, a wide range of energy conversion systems such as generators, high-performance electric engines, and electric vehicles, are highly reliant on the advancement of soft computing techniques used in rotating electrical machines. This article presents the-state-of-the-art of soft computing techniques and their applications, which have greatly influenced the progression of this significant realm of energy. Through a novel taxonomy of systems and applications, the most critical advancements in the field are reviewed for providing an insight into the future of control and design of rotating electrical machines.

show abstract

Section: Hybrid Soft Computing Methodsmentioning

confidence: 99%

Review of Soft Computing Models in Design and Control of Rotating Electrical Machines

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Such models are usually developed by the aid of 'design of experiments' techniques. The application of these approximated models is rapidly expanding for modern electrical machines and especially FSPMs [135,136].…”

Section: Fes and Surrogate Modelsmentioning

confidence: 99%

Performance modifications and design aspects of rotating flux switching permanent magnet machines: a review

Nobahari

Aliahmadi

Faiz

2019

IET Electric Power Applications

View full text Add to dashboard Cite

Permanent magnet flux switching machines (FSPMs) have attracted considerable attention in recent years, due to their outstanding characteristics to operate as brushless AC drives. Since they are relatively new, there are particular challenges not only for their design procedure but also for their operating principle comprehension. This study highlights several issues on FSPMs from the basic operation description to their key design points. After a comprehensive description of FSPMs working principles, a detailed discussion is presented on their armature winding design. In addition, this study reviews various methods for their performance modification, including torque profile, electromotive force, and loss characteristics. This study also surveys field regulation technologies, modelling and design optimisation methods applied to FSPMs along with their fabrication and cost issues.

show abstract

“…Then, the optimization algorithms can apply to these models 9 . The popular approximating models are kriging model (KM), response surface methodology (RSM), and artificial neural network (ANN) 10‐12 All of the approximating models need the initial samples data.…”

Section: Introductionmentioning

confidence: 99%

“…In References 14 and 23, the intelligent search algorithm (ISA) is used directly to collect the FEM samples and optimization procedure, which leads to increment in the time consumption of the optimization procedure. While in References 12 and 24 the surrogate models (SM), in References 25 and 26 the RSM model, and in Reference 27 the KM are implemented to approximate the relation between design variables and objectives then the ISA is used to geometry optimization, which reduced the required FEM samples. Recently, papers in the optimization of the FSPM motor are focused on the capabilities of this structure for use in electric vehicle application.…”

Section: Introductionmentioning

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Optimal design of permanent magnet flux switching generator for wind applications via artificial neural network and multi-objective particle swarm optimization hybrid approach

Cited by 38 publications

References 41 publications

Review of Soft Computing Models in Design and Control of Rotating Electrical Machines

Review of Soft Computing Models in Design and Control of Rotating Electrical Machines

Performance modifications and design aspects of rotating flux switching permanent magnet machines: a review

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

Contact Info

Product

Resources

About