Physics-Informed Induction Machine Modelling
This work addresses the need for more reliable AI-based simulations in electrical engineering, though it appears incremental by integrating physics with existing neural network techniques.
The paper tackled the problem of simulating electromagnetic transients in induction machines by developing a Neural Induction Machine (NeuIM) model using physics-informed machine learning, achieving improved accuracy over purely data-driven methods as validated in case studies.
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.