Physics-Aware Neural Dynamic Equivalence of Power Systems

TL;DR

This paper introduces a physics-aware neural network approach called NeuDyE for creating dynamic equivalences of power systems, maintaining their behavior after disturbances through data-driven and physics-informed learning methods.

Contribution

It proposes a novel ODE-Net-based framework for power system equivalence, incorporating physics-informed and physics-guided learning to improve accuracy and applicability.

Findings

NeuDyE accurately models power system dynamics after disturbances.

The physics-informed approach improves closed-loop accuracy.

NeuDyE is effective across various contingencies in case studies.

Abstract

This letter devises Neural Dynamic Equivalence (NeuDyE), which explores physics-aware machine learning and neural-ordinary-differential-equations (ODE-Net) to discover a dynamic equivalence of external power grids while preserving its dynamic behaviors after disturbances. The contributions are threefold: (1) an ODE-Net-enabled NeuDyE formulation to enable a continuous-time, data-driven dynamic equivalence of power systems; (2) a physics-informed NeuDyE learning method (PI-NeuDyE) to actively control the closed-loop accuracy of NeuDyE without an additional verification module; (3) a physics-guided NeuDyE (PG-NeuDyE) to enhance the method's applicability even in the absence of analytical physics models. Extensive case studies in the NPCC system validate the efficacy of NeuDyE, and, in particular, its capability under various contingencies.