Design Improvement of Permanent Magnet Motor Using Single and Multi-Objective Approach
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Ss. Cyril and Methodius University, Faculty of Electrical Engineering and Information Technologies, Department of Electrical Machines, Transformers and Apparatuses, Skopje, North Macedonia
Power Electronics and Drives 2024;9 (44):34-49
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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.