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Optimization of the permanent magnet machine based on the artificial inteligence

By Jiří Kurfürst

Abstract

The dissertation thesis deal with the design and the optimization of the permanent magnet synchronous machine (SMPM) based on the artificial intelligence. The main target is to apply potential optimization methods on the design procedure of the machine and evaluate the effectiveness of optimization and the optimization usefulness. In general, the optimization of the material properties (NdFeB or SmCo), the efficiency maximization with given nominal input parameters, the cogging torque elimination are proposed. Moreover, the magnet shape optimization, shape of the air gap and the shape of slots were also performed. The well known Genetic algorithm and Self-Organizing migrating algorithm produced in Czech were presented and applied on the particular optimization issues. The basic principles (iterations) and definitions (penalty function and cost function) of proposed algorithms are demonstrated on the examples. The results of the vibration generator optimization (VG) with given power 7mW (0.1g acceleration) and the results of the SMPM 1,1kW (6 krpm) optimization are practically evaluated in the collaboration with industry. Proposed methods are useful for the optimization of PM machines and they are further theoretically applied on the low speed machine (10 krpm) optimization and high speed machine (120 krpm) optimization

Topics: penalty function; SOMA; ATO; SmCo; cogging moment; vibration generator; Genetic algorithm; harmonics; magnety.; ztráty; cogging torque; SMPM; SOMA; ATO; NdFeB; SmCo; Vibrační generátor; servo motor; nízkootáčkové stroje; vysokootáčkové stroje; optimalizace; umělá inteligence; Genetický Algoritmus; penalizační funkce; ohodnocovací funkce; zvyšování účinnosti; maximization of efficiency; magnets.; vyšší harmonické; low speed machines; NdFeB; servo machines; high speed machines; losses; SMPM; optimization; artificial intelligence; cost function
Publisher: Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií
Year: 2013
OAI identifier: oai:invenio.nusl.cz:233585
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