Gallium Nitride Power Electronic Devices Modeling Using Machine Learning

A state-of-the-art Machine Learning (ML) based approach, by modeling the behavior of Gallium Nitride (GaN) power electronic devices, is presented in this paper. Switching voltage and current waveforms of these novel devices are accurately predicted using the developed supervised ML algorithm. This was utilised to build a more generic black-box model for these devices. Moreover, long short-term memory unit (LSTM) and gated recurrent unit (GRU) device models have been proposed to make the approach more user friendly. The performance of the developed approach is verified using a set of simulations and experimental tests under 450 V, 10 A test conditions. Model results demonstrate an error rate of 0.03 and convergence speed of 3s with excellent stability. Compared to the existing models, the developed ML-based model produces more accurate results, converges faster and has a better stability. Additionally, the developed ML-based GaN model offers the ability to select the best fit available GaN model (Panasonic, GaN Systems, Transphorm etc.). It automatically configures them into a system that would optimally yield the desired power conversion. This enables a shorter learning curve for the power electronics community, which would lead to acceptance and faster adoption of these devices by the power electronics industry.