Simulation of a closed-loop dc-dc converter using a physics-informed neural network-based model
This work addresses the need for improved simulation tools in power electronics, offering a potential alternative to existing methods, though it appears incremental as it builds on prior physics-informed and data-driven approaches.
The paper tackled the challenge of fast and accurate time-domain simulation for power electronics by proposing a physics-informed bidirectional LSTM neural network (BiLSTM-PINN) to simulate a closed-loop dc-dc boost converter, showing it outperformed a fully-connected neural network by over 9 times in median RMSE and had more than 2.6 times smaller standard deviation.
The growing reliance on power electronics introduces new challenges requiring detailed time-domain analyses with fast and accurate circuit simulation tools. Currently, commercial time-domain simulation software are mainly relying on physics-based methods to simulate power electronics. Recent work showed that data-driven and physics-informed learning methods can increase simulation speed with limited compromise on accuracy, but many challenges remain before deployment in commercial tools can be possible. In this paper, we propose a physics-informed bidirectional long-short term memory neural network (BiLSTM-PINN) model to simulate the time-domain response of a closed-loop dc-dc boost converter for various operating points, parameters, and perturbations. A physics-informed fully-connected neural network (FCNN) and a BiLSTM are also trained to establish a comparison. The three methods are then compared using step-response tests to assess their performance and limitations in terms of accuracy. The results show that the BiLSTM-PINN and BiLSTM models outperform the FCNN model by more than 9 and 4.5 times, respectively, in terms of median RMSE. Their standard deviation values are more than 2.6 and 1.7 smaller than the FCNN's, making them also more consistent. Those results illustrate that the proposed BiLSTM-PINN is a potential alternative to other physics-based or data-driven methods for power electronics simulations.