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Title: Thermal management of permanent magnet electric machines : an integrated approach of design, monitoring and control
Author: Hey Heng Kiat, Jonathan
ISNI:       0000 0004 5355 7531
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2014
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The widespread application of electric machines across different industries have a large impact on the operation cost and energy usage. This has driven research to improve the performance of electric machines in terms of the power density, efficiency and reliability. A comprehensive method of thermal management integrating the design, monitoring and control of electric machines is proposed in this research. The method is applied to two permanent magnet motors - a high power axial flux motor and a high precision linear motor. Firstly, a two stage optimization technique is applied to the design of the linear motor. A first stage global search using Genetic Algorithm followed by a second stage Branch and Bound method is a systematic way of searching for the optimal feasible solution. It resulted in an improved design which is more compact in size and produces a higher thrust force (39.9%) while reducing the heat generation (26.2%) when compared to an initial design. The design optimization takes into consideration the multi-physics interactions using a reduced order model. This resulted in a computation time saving of 80% over a commercial software optimization package while modelling accuracy is maintained through an output space mapping technique. During a continuous 5 hour cyclic positioning application, a model based compensation method is applied to the linear motor for real time thermal disturbance rejection. It resulted in improved positioning accuracy with a final mean unidirectional position deviation of −0.2μm and repeatability of ±0.7μm. Effective disturbance rejection is achieved through accurate disturbance modelling while using minimal sensor measurements. The minimal realization of the compensation model is achieved through model identification. In addition, a Modified Kalman Filter is proposed which led to a reduction in the number of temperature sensors required. Lastly, an experimentally determined lumped parameter thermal model is used for condition monitoring of the high power motor. Components of the electromagnetic losses are derived from a parameter estimation method using temperature measurement as input to the model. The method is able to detect input current fluctuations during a drive cycle which makes it possible to identify faults like a short circuit. Moreover, the model is useful for real time temperature monitoring which provides thermal protection against transient overloads. The modelling accuracy is improved by using a model identification technique to determine the thermal parameters.
Supervisor: Martinez-Botas, Ricardo Sponsor: Agency for Science, Technology and Research, Singapore
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available