Design Optimization Methods for Electrical Machines: A Review

Omar, Mohd Fairoz; Sulaiman, Erwan; Soomro, Irfan Ali; Ahmad, M. Z.; Aziz, Roziah

doi:10.1007/s42835-022-01358-y

Cited by 5 publications

(6 citation statements)

References 86 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It involves dividing the initial design space into multiple lowerdimensional subspaces and sequentially optimizing the variables within each subspace. This method has been demonstrated to be highly effective in design optimization problems, offering several advantages, such as reducing the design cycle, lowering computational costs, and improving overall design efficiency [94]. The flowchart in Fig.…”

Section: B Multi-level Optimization Methodsmentioning

confidence: 99%

“…This iterative process helps to refine the design and improve the optimization objectives. This allows for systematic exploration of the design space, enabling the identification of optimal solutions [94].…”

Section: Sequential Optimization Methodsmentioning

confidence: 99%

“…Furthermore, genetic algorithms (GA), differential evolution (DE) algorithms, and particle swarm optimization (PSO) are the three major subclasses of evolutionary algorithms (EA) frequently utilized. In the category of multi-objective algorithms (MOA), the widely used modified PSO (MPSO), non-dominated sorting genetic algorithm (NSGA II), hybrid multi-objective algorithm (hybrid MOA), and response Surface-PSO (RS-PSO) is among the four commonly employed optimization approaches [86], [94]. These algorithms have been effective for addressing multiple objectives simultaneously.…”

Section: Assumes Linear Relationshipsmentioning

confidence: 99%

“…The solution candidates were then evaluated and processed in a distinct manner, as explained in the preceding section. By considering multiple objectives, the optimization process aims to find a set of solutions that represents a trade-off between different performance metrics, allowing for a more comprehensive and flexible design approach [94].…”

Section: Assumes Linear Relationshipsmentioning

confidence: 99%

See 3 more Smart Citations

Advancements in Flux Switching Machine Optimization: Applications and Future Prospects

Abunike,

Okoro,

Far

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Flux switching machines play a crucial role in emerging sectors such as renewable energy, electric vehicles, and the aerospace industry. Recent advancements have underscored their potential for achieving high torque density, effective heat management, and flux-weakening capabilities. This paper provides a comprehensive review of the design optimization of these machines, offering a unique contribution, as no previous attempts have delved into a detailed exploration of various optimization techniques specific to this technology. Special attention is given to state-of-the-art technologies related to multi-objective optimization and the performance analysis of the three variants: permanent magnet, wound-field, and hybrid excited. The review highlights and compares several optimization strategies from recent research, revealing research gaps in integrating advanced control algorithms, thermal, vibroacoustic, and structural models into the optimization design process of these machines. Furthermore, this review identifies essential trends in optimization development, offering valuable insights and future perspectives for these machines across diverse applications.

show abstract

Section: B Multi-level Optimization Methodsmentioning

confidence: 99%

Section: Sequential Optimization Methodsmentioning

confidence: 99%

Section: Assumes Linear Relationshipsmentioning

confidence: 99%

Section: Assumes Linear Relationshipsmentioning

confidence: 99%

See 2 more Smart Citations

Advancements in Flux Switching Machine Optimization: Applications and Future Prospects

Abunike,

Okoro,

Far

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…This is due to the necessity of many gradient computations, whose number depends on the dimension of the design space. Furthermore, there is a risk that such algorithms end up in local optima [14].…”

Section: Introductionmentioning

confidence: 99%

Single-Objective Machine Design with Gaussian Process Regression and Bayesian Optimization (Extended Version)

Rossmann,

Kamper,

Hackl

2024

Preprint

View full text Add to dashboard Cite

Due to the complex rotor design of reluctance synchronous machines, a finite element analysis is essential for the accurate calculation of machine relevant performance objectives. Reluctance synchronous machines tend to have a large torque ripple, if this objective is not considered during the machine design. This necessitates a large number of simulation steps, resulting in a high computational burden and a long simulation time per design evaluation. Therefore, an efficient optimization algorithm is required. This paper proposes a complete framework for single-objective machine design optimization using Gaussian process regression and Bayesian optimization. Focusing on reluctance synchronous machine design, different kernel functions (squared exponential, Matern, rational quadratic) and hyperparameter configurations ´ are assessed for regression accuracy of the optimization objectives mean torque, torque ripple, and power factor. The impact of noise in the input data on the regression results is also investigated. Bayesian optimization with the infill criterion Expected Improvement is finally used to perform machine design optimization for 18 design variables. Bayesian optimization outperforms the classical algorithms such as genetic or particle swarm algorithms. It results in a faster design optimization, even for such a high number of design variables.

show abstract

Spline-Based Rotor and Stator Optimization of a Permanent Magnet Synchronous Motor

Wiesheu,

Komann,

Merkel

et al. 2024

2024 International Conference on Electrical Machines (ICEM)

View full text Add to dashboard Cite

Design Optimization Methods for Electrical Machines: A Review

Cited by 5 publications

References 86 publications

Advancements in Flux Switching Machine Optimization: Applications and Future Prospects

Advancements in Flux Switching Machine Optimization: Applications and Future Prospects

Single-Objective Machine Design with Gaussian Process Regression and Bayesian Optimization (Extended Version)

Spline-Based Rotor and Stator Optimization of a Permanent Magnet Synchronous Motor

Contact Info

Product

Resources

About