Physics-Informed Neural Networks in Power System Dynamics: Improving Simulation Accuracy

TL;DR

This paper explores the use of Physics-Informed Neural Networks (PINNs) to enhance the accuracy of power system dynamic simulations, especially under fast and unpredictable renewable energy-driven responses.

Contribution

It introduces PINNs as a novel, integrated approach for power system simulation, replacing traditional models to improve accuracy and efficiency.

Findings

PINNs improve simulation accuracy over larger time steps.

Replacing synchronous machines with PINNs enhances system state predictions.

PINNs effectively model fast, unpredictable power electronics dynamics.

Abstract

The importance and cost of time-domain simulations when studying power systems have exponentially increased in the last decades. With the growing share of renewable energy sources, the slow and predictable responses from large turbines are replaced by the fast and unpredictable dynamics from power electronics. The current existing simulation tools require new solutions designed for faster dynamics. Physics-Informed Neural Networks (PINNs) have recently emerged in power systems to accelerate such simulations. By incorporating knowledge during the up-front training, PINNs provide more accurate results over larger time steps than traditional numerical methods. This paper introduces PINNs as an alternative approximation method that seamlessly integrates with the current simulation framework. We replace a synchronous machine for a trained PINN in the IEEE 9-, 14-, and 30-bus systems and simulate several network disturbances. Including PINNs systematically boosts the simulations' accuracy, providing more accurate results for both the PINN-modeled component and the whole multi-machine system states.