Temporal Modeling for Power Converters With Physics-in-Architecture Recurrent Neural Network

Existing time-series data-driven approaches for converter modeling are data-intensive, uninterpretable, and lack out-of-domain extrapolation capability. Recent physics-informed modeling methods combine physics into data-driven models using loss functions, but they inherently suffer from physical inconsistency, lower modeling accuracy, and require resource-intensive retraining for new case predictions. Consequently, catering for the challenges in current data-driven and physics-informed models, this article proposes a physics-in-architecture recurrent neural network (PA-RNN) for the time-domain modeling of power converters. The proposed PA-RNN consists of a physics-in-architecture core and a data-driven core in parallel. The physics-in-architecture core rigorously integrates circuit physical laws into its customized recurrent neural architecture by leveraging numerical differentiation, while a gated recurrent unit with layer normalization serves as the data-driven core to compensate for converter behaviors not characterized by physics. The PA-RNN modeling process is explained in detail with a design case. As 1-kW hardware and comprehensive algorithm experiments have verified the superiority of PA-RNN. Overall, PA-RNN is explainable and data-light as well as possesses good domain transfer capability to assess out-of-domain scenarios without training. This article envisions to democratize artificial intelligence for the modeling of power electronics systems.