Physics-Informed Induction Machine Modelling
Qing Shen Yifan Zhou Peng Zhang
This rapid communication devises a Neural Induction Machine (NeuIM) model,
which pilots the use of physics-informed machine learning to enable AI-based
electromagnetic transient simulations. The contributions are threefold: (1) a
formation of NeuIM to represent the induction machine in phase domain; (2) a
physics-informed neural network capable of capturing fast and slow IM dynamics
even in the absence of data; and (3) a data-physics-integrated hybrid NeuIM
approach which is adaptive to various levels of data availability. Extensive
case studies validate the efficacy of NeuIM and in particular, its advantage
over purely data-driven approaches.