Robust Design Optimization of Electrical Machines and Devices

• Other Authors: Orosz, Tamás (Editor), Pánek, David (Editor), Rassõlkin, Anton (Editor), Kuczmann, Miklos (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Subjects: Technology: general issues; History of engineering & technology; Energy industries & utilities; design optimization; genetic algorithms; Taguchi designs; electromagnetic coupling; additive manufacturing; dynamic performance; equivalent circuit model; finite-element method; linear induction motor; longitudinal end effect; transverse edge effect; DLIM; equivalent circuit; end effect; thrust; finite element; insulation core transformer; non-uniformity of disk output voltage; load regulation; voltage compensation; dummy primary winding; PSO algorithm; PMSM; analytical method; perturbation theory; torque; torque ripple; rotor eccentricity; optimization; electrical machines; finite element method; nuclear power plant; thermal degradation; thermal-mechanical aging; low-voltage cables; polymer degradation; dielectric spectroscopy; hardness; hairpin windings; multi-objective optimizations; Finite Element Method; Lumped Parameter Model; Permanent Magnet Synchronous Reluctance Motor; rotor flux barrier; torque development; Aurivillius phases; Bi3−xNdxTi1.5W0.5O9; activation energy Ea; Curie temperature TC; artificial intelligence; optimization techniques; self-organizing map; power quality; wind generation; upgraded insulant; ester oil-based nano fluids; thermal conductivity; relative viscosity; nanoparticles effect; alternative fluid; temperature change; model predictive control; hysteresis current control; execution time; synchronous reluctance motors; antenna array; synthesis control; quantized control; array factor; n/a
• Online Access: DOAB: download the publication; DOAB: description of the publication

This reprint contains fourteen chosen articles on robust design optimization of electrical machines and devices. Optimization is essential for the research and design of electromechanical devices, especially electrical machines. Finding optimal solutions may lead to cheaper and more efficient production of electrical machines. However, optimizing such a complex system as an electrical machine is a computationally expensive optimization problem, where many physical domains should be considered together. However, a good, practical design should be insensitive to parameter changes and the manufacturing tolerances. The collected papers show how modern artificial intelligence (AI) tools can be used for the robust design optimization of electric machines and electrical devices. The articles which are published in this Special Issue present the latest results of current research fields. Hopefully, the presented models and various application fields will provide useful information for researchers and professionals interested in these techniques themselves or who have other problems from different fields.