Power System Dynamic Simulation Using Fourier Neural Operators (Papers Track)
Wenqi Cui (University of Washington); Weiwei Yang (Microsoft Research); Baosen Zhang (University of Washington)
With the ambition of reducing carbon emissions and mitigating climate change, many regions have set up the goal to generate electricity with close to 100% renewables. However, actual renewable generations are often curtailed by operators because it is too hard to check the dynamic stability of electric grid under the high uncertainties introduced by the renewables. The dynamics of a power grid are governed by a large number of nonlinear ordinary differential equations (ODEs). To safely operate the system, operators need to check that the states described by this set of ODEs stay within prescribed limits after various potential faults. But solving these ODEs are very time-consuming using current numerical solvers and machine learning approaches have been proposed to reduce the computational time. Current methods generally suffer from overfitting and failures to predict unstable behaviors. This paper proposes a novel framework for power system dynamic simulation using Fourier neural operator to learn in the frequency domain. The system topology and fault information are encoded through a 3D Fourier transform. We show that the proposed approach can speed up the computation by orders of magnitude while also remain high accuracy for different fault types.