Physics-Informed Induction Machine Modelling

TL;DR

This paper introduces a physics-informed neural network model called NeuIM for electromagnetic transient simulations of induction machines, capable of capturing complex dynamics with limited data and validated through extensive case studies.

Contribution

It presents a novel hybrid NeuIM approach that integrates physics and data, enabling accurate modeling of induction machines without extensive data.

Findings

NeuIM effectively captures fast and slow dynamics of induction machines.

NeuIM outperforms purely data-driven models in validation studies.

The hybrid approach adapts to various data availability levels.

Abstract

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.