PINNSim: A Simulator for Power System Dynamics based on Physics-Informed Neural Networks

TL;DR

PINNSim introduces a physics-informed neural network-based simulator that significantly increases time step sizes in power system dynamics simulations, potentially reducing computational costs compared to traditional methods.

Contribution

The paper presents PINNSim, a novel neural network-based simulator that enables larger time steps for power system dynamic simulations, improving efficiency over conventional methods.

Findings

PINNSim achieves larger time steps than traditional trapezoidal integration.

Demonstrated on a 9-bus system, PINNSim accelerates simulation times.

Key steps outlined for developing a comprehensive PINNSim platform.

Abstract

The dynamic behaviour of a power system can be described by a system of differential-algebraic equations. Time-domain simulations are used to simulate the evolution of these dynamics. They often require the use of small time step sizes and therefore become computationally expensive. To accelerate these simulations, we propose a simulator - PINNSim - that allows to take significantly larger time steps. It is based on Physics-Informed Neural Networks (PINNs) for the solution of the dynamics of single components in the power system. To resolve their interaction we employ a scalable root-finding algorithm. We demonstrate PINNSim on a 9-bus system and show the increased time step size compared to a trapezoidal integration rule. We discuss key characteristics of PINNSim and important steps for developing PINNSim into a fully fledged simulator. As such, it could offer the opportunity for significantly increasing time step sizes and thereby accelerating time-domain simulations.