Graduation Semester and Year

2021

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Electrical Engineering

Department

Electrical Engineering

First Advisor

Ali Davoudi

Abstract

Electric machines are the most important element in the power grid. Given its centennial legacy and the rise of electric vehicles and distributed energy resources, it is imperative to bring new technologies into this area. This work tries to bridge the gap between electric machines and innovative research domains such as convex optimization and FPGA-based hardware acceleration. Problems of electric machine parameter identification and real-time simulation are considered. A convex optimization-based framework is designed to identify machine parameters. This tool is used to perform the macromodeling of a synchronous machine from its magnetic-equivalent circuit model. Furthermore, it is used to obtain induction machine parameters using limited and non-intrusive measurements. Given that optimization-based methods are usually offline, partial-update Kalman filter is investigated for online electric machine state and parameter estimation. Finally, the hardware acceleration of electric machine models executed on FPGA is studied.

Keywords

Electric machines, Convex optimization, Induction motor, PMSM, Kalman filter, FPGA

Disciplines

Electrical and Computer Engineering | Engineering

Comments

Degree granted by The University of Texas at Arlington

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