LAUSR

DC traction drive systems require high-frequency switching in the power converter whose device-level switching transients have a significant impact on the accuracy of hardware-in-the-loop emulation. Real-time device-level emulation has high computation demand for calculating the switch on and off transients. This paper introduces a new method to estimate the switching transients by utilizing artificial intelligence in the hardware design. In the hybrid neural network, the $k$ -nearest neighbors ( $k$ NN) concept and the recurrent neural network (RNN) have been employed to emulate the transient waveforms in the DC traction drive. The $k$ NN module classifies the switching states while the RNN module predicts the transient current for a specific condition. This work also proves that the classification of the input switching states with the help of $k$ NN can play an essential role. The hardware implementation of the study case can be executed at a time-step of $100~ns$ with device-level transients. The results have been validated by PSCAD/EMTDC^® at system-level and SaberRD^® at device-level.