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Baig, Sheraz

University of British Columbia

Variable frequency drives (VFDs) of low- and high-power levels are widely utilized in many commercial, industrial, vehicular, and propulsion applications, wherein typically, a squirrel-cage induction motor is fed from an inverter through a cable. To design and tune such systems, it is essential to develop efficient, accurate models of induction machines for studying the motor-converter interactions and low-to-high frequency phenomena. Depending on the required fidelity level, study objectives, and frequency range of interest, several classes of models of induction machines have been proposed in the literature, which can be generally classified into low-frequency and high-frequency models. Moreover, the motor-converter systems are typically the computational bottleneck in electromagnetic transient (EMT) simulators that are used in the power industry. It is desirable to have an induction machine model capable of capturing all transient phenomena in the range from dc to 10 MHz and can be effectively used for the analysis of VFD systems. This thesis is focused on advancing the state-of-the-art induction machine and VFD modelling in state-variable-based (SVB) EMT programs. Specifically, this thesis presents an efficient modelling approach for system-level studies of VFDs, a reconfigurable star-delta CPVBR model for studying star-delta starting transients in motors, and a wideband decoupled constant-parameter VBR model capable of studying motor-converter interactions in the range from dc to 10 MHz. Computer simulations and experimental studies of the VFD systems demonstrate that the proposed models represent advantages over existing/conventional models. It is also envisioned that proposed models will become a valuable asset for offline and real-time EMT simulators that are used for transient studies of machine-converter interactions in marine power systems, mining and oil drilling sites, and other applications with VFD systems.

Text

2023-08-23

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/85596

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/