MACROMODELING AND ACCELERATED SIMULATIONS OF ELECTRIC MACHINES

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.