PINNSim: A Simulator for Power System Dynamics based on Physics-Informed Neural Networks
This addresses the problem of slow time-domain simulations for power system engineers, but it is incremental as it builds on existing PINN methods.
The authors tackled the computational expense of power system dynamics simulations by proposing PINNSim, a simulator based on Physics-Informed Neural Networks that allows significantly larger time steps, demonstrated on a 9-bus system with increased step size compared to trapezoidal integration.
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.