Physics-Informed Neural Networks: a Plug and Play Integration into Power System Dynamic Simulations

TL;DR

This paper introduces a novel approach to integrate Physics-Informed Neural Networks into power system simulations, significantly enhancing efficiency and accuracy for multi-component dynamic analysis.

Contribution

It presents a new training formulation and integration method for PINNs in multi-component power system simulations, enabling longer time steps and faster computations.

Findings

PINNs can replace classical numerical methods for system components.

The integrated approach accelerates simulations by increasing time steps.

Demonstrated effectiveness on IEEE 9-bus system.

Abstract

Time-domain simulations are crucial for ensuring power system stability and avoiding critical scenarios that could lead to blackouts. The next-generation power systems require a significant increase in the computational cost and complexity of these simulations due to additional degrees of uncertainty, non-linearity and states. Physics-Informed Neural Networks (PINN) have been shown to accelerate single-component simulations by several orders of magnitude. However, their application to current time-domain simulation solvers has been particularly challenging since the system's dynamics depend on multiple components. Using a new training formulation, this paper introduces the first natural step to integrate PINNs into multi-component time-domain simulations. We propose PINNs as an alternative to other classical numerical methods for individual components. Once trained, these neural networks approximate component dynamics more accurately for longer time steps. Formulated as an implicit and consistent method with the transient simulation workflow, PINNs speed up simulation time by significantly increasing the time steps used. For explanation clarity, we demonstrate the training, integration, and simulation framework for several combinations of PINNs and numerical solution methods using the IEEE 9-bus system, although the method applies equally well to any power system size.