Amir Ebrahimi

Hybrid Analytical Modeling and Optimization of Surface Mounted Permanent Magnetic Synchronous Motors Considering Spatial Harmonics


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ISBN:

978-3-8440-5026-4

Reihe:

Institut für Elektrische Energiewandlung
Herausgeber: Prof. Dr.-Ing. Nejila Parspour
Stuttgart

Band:

Schlagwörter:

Analytical Modeling; Spatial Harmonics; Motors

Publikationsart:

Dissertation

Sprache:

Englisch

Seiten:

134 Seiten

Abbildungen:

72 Abbildungen

Gewicht:

210 g

Format:

21 x 14,8 cm

Bindung:

Paperback

Preis:

45,80 € / 57,30 SFr

Erscheinungsdatum:

Februar 2017

Kaufen:

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Zusammenfassung:

This work presents a new analytical modeling method of electrical machines with a focus on Permanent Magnet Synchronous Motors. The goal of this work is to establish a platform for simulation and optimization of synchronous motors considering the spatial distribution of flux density in all its parts. The dynamic motor behavior is emulated with a quasi-static model. Based on this model, all other motor parameters are calculated and presented as mathematical functions of time and rotor position. The spatial harmonic model of the motor facilitates a deeper understanding of motor behavior. Consequently, several chapters of this work result from the knowledge generated by the help of this model. The new description of torque, induced voltage and a novel control algorithm introduced in this work are some examples of the advantage of the presented analytical model. The primary goal of this work is to develop an analytical model which is comparable to the conventional finite element method. The adaptive algorithm introduced here can be modified based on the results obtained from finite element analyses. The compliance of the analytical model to finite element calculations is the main goal of this work. Finally the results are compared to the measurements done on a handmade experimental model. The different control algorithms are realized using an experimental test bench.
An intelligent optimization algorithm is developed and matched to the analytical model. The optimization algorithm facilitates a multi-objective optimization of motor parameters.