<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Optimisation, or optimal design, has become a fundamental aspect of engineering across various domains, including power devices, power systems, and industrial systems. Engineers and academics have been actively involved in optimising these systems to achieve better performance, efficiency, and cost-effectiveness. Optimising electrical machines, including permanent magnet motors, is a complex task. It often involves solving intricate problems with various parameters and constraints. Engineers use different optimisation methods to tackle these challenges. Depending on the specific requirements and goals of a design project, engineers may employ either single-objective or multi-objective optimisation approaches. Single-objective optimisation focuses on optimising a single objective, while multi-objective optimisation considers multiple conflicting objectives. In optimisation, objective functions are mathematical representations of what needs to be optimised. In this case, optimising the efficiency of the motor, reducing cogging torque, and minimising the total weight of active materials are defined as possible objective functions. Genetic algorithms are nature based algorithms that are commonly used in engineering to find optimal solutions to complex problems, including those with multiple objectives. In this paper, after conducting optimisations using different objective functions and methods, a comparative analysis of the results is performed. This helps in understanding the trade-offs and benefits of different design choices. Finite element analysis (FEA) is a computational method used to analyse the physical properties and behaviours of complex structures and systems. In this case, FEA is used to validate and analyse selected optimisation solutions to ensure they meet the desired characteristics and parameters. Overall, this work demonstrates the interdisciplinary nature of engineering, where mathematics, computer science (for optimisation algorithms), and physics (for FEA) converge to improve the performance and efficiency of electrical machines. It also underscores the importance of considering multiple objectives in design processes to find optimal solutions that strike a balance between competing goals.</p>
</abstract>

Optimisation, or optimal design, has become a fundamental aspect of engineering across various domains, including power devices, power systems, and industrial systems. Engineers and academics have been actively involved in optimising these systems to achieve better performance, efficiency, and cost-effectiveness. Optimising electrical machines, including permanent magnet motors, is a complex task. It often involves solving intricate problems with various parameters and constraints. Engineers use different optimisation methods to tackle these challenges. Depending on the specific requirements and goals of a design project, engineers may employ either single-objective or multi-objective optimisation approaches. Single-objective optimisation focuses on optimising a single objective, while multi-objective optimisation considers multiple conflicting objectives. In optimisation, objective functions are mathematical representations of what needs to be optimised. In this case, optimising the efficiency of the motor, reducing cogging torque, and minimising the total weight of active materials are defined as possible objective functions. Genetic algorithms are nature based algorithms that are commonly used in engineering to find optimal solutions to complex problems, including those with multiple objectives. In this paper, after conducting optimisations using different objective functions and methods, a comparative analysis of the results is performed. This helps in understanding the trade-offs and benefits of different design choices. Finite element analysis (FEA) is a computational method used to analyse the physical properties and behaviours of complex structures and systems. In this case, FEA is used to validate and analyse selected optimisation solutions to ensure they meet the desired characteristics and parameters. Overall, this work demonstrates the interdisciplinary nature of engineering, where mathematics, computer science (for optimisation algorithms), and physics (for FEA) converge to improve the performance and efficiency of electrical machines. It also underscores the importance of considering multiple objectives in design processes to find optimal solutions that strike a balance between competing goals.

Design Improvement of Permanent Magnet Motor Using Single- and Multi-Objective Approaches

Ss. Cyril and Methodius University, Faculty of Electrical Engineering and Information Technologies

Department of Electrical Machines, Transformers and Apparatuses

Power Electronics and Drives

This work is licensed under the Creative Commons Attribution 4.0 International License.

Optimisation, or optimal design, has become a fundamental aspect of engineering across various domains, including power devices, power systems, and industrial...

Design Improvement of Permanent Magnet Motor Using Single- and Multi-Objective Approaches

Online veröffentlicht: 20. Jan. 2024

Seitenbereich: 34 - 49

Eingereicht: 25. Aug. 2023

Akzeptiert: 11. Dez. 2023

DOI: https://doi.org/10.2478/pead-2024-0003

Schlüsselwörter
permanent magnet motor, efficiency, cogging torque, multi-objective, optimisation

© 2024 Goga Cvetkovski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Design Improvement of Permanent Magnet Motor Using Single- and Multi-Objective Approaches

Online veröffentlicht: 20. Jan. 2024

Seitenbereich: 34 - 49

Eingereicht: 25. Aug. 2023

Akzeptiert: 11. Dez. 2023

DOI: https://doi.org/10.2478/pead-2024-0003

Schlüsselwörterpermanent magnet motor, efficiency, cogging torque, multi-objective, optimisation

© 2024 Goga Cvetkovski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
permanent magnet motor, efficiency, cogging torque, multi-objective, optimisation