This paper presents a digital hardware realization of a real-time simulator for a complete induction machine drive using a field-programmable gate array (FPGA) as the computational engine. The simulator was developed using Very High Speed Integrated Circuit Hardware Description Language (VHDL), making it flexible and portable. A novel device-characteristic based model suitable for FPGA implementation has been proposed for the 2-level 6-pulse IGBT-based voltage-source converter (VSC). The VSC model is computed at a fixed time-step of 12.5 ns allowing a highly detailed and precise accounting of gating signals. The simulator also models a squirrel cage induction machine, a direct field-oriented control system, a space-vector pulse-width modulation scheme (SVPWM) and a measurement system. A multirate simulation of the system shows the slow (machine) as well as the fast (VSC and control) dynamic components. Real time simulation results under steady-state and transient conditions demonstrate modeling accuracy and efficiency.

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This paper presents a digital hardware realization of a real-time simulator for a complete induction machine drive using a field-programmable gate array (FPGA) as the computational engine. The simulator was developed using Very High Speed Integrated Circuit Hardware Description Language (VHDL), making it flexible and portable. A novel device-characteristic based model suitable for FPGA implementation has been proposed for the 2-level 6-pulse IGBT-based voltage-source converter (VSC). The VSC model is computed at a fixed time-step of 12.5 ns allowing a highly detailed and precise accounting of gating signals. The simulator also models a squirrel cage induction machine, a direct field-oriented control system, a space-vector pulse-width modulation scheme (SVPWM) and a measurement system. A multirate simulation of the system shows the slow (machine) as well as the fast (VSC and control) dynamic components. Real time simulation results under steady-state and transient conditions demonstrate modeling accuracy and efficiency.

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A new approach for induction motor drive control is presented in this paper. The new scheme is based on the direct application of an artificial neural network, trained with sliding mode control, into the feedback control system. Neural network learning is implemented with an on-line adaptation algorithm that inherits robustness and high speed of learning from Sliding Mode Control. The results showed that proportional and integral or proportional, integral and differential controllers used in classical motor drives can be replaced with a neural network with on-line learning.