Neural ODE Model of Power Electronic Converters With Accelerated Computation and High Fidelity

Nowadays, conducting detailed numerical simulation of power electronic (PE) converters is time-consuming due to the iterative solution of nonlinear equations. While simplified numerical models can reduce the computational burden, they are unable to guarantee fidelity in highly nonlinear systems. To accelerate the detailed numerical simulation while maintaining high fidelity, this paper proposes a novel data-driven approach to modeling PE converters based on neural ordinary differential equations (ODE). This model eliminates the iterative solution of nonlinear equations by being data-driven and thus accelerates the simulation. In addition, to enhance the model fidelity, this approach incorporates prior knowledge about numerical ODE solvers into the model structures and distinguishes multi-scale characteristics from the dataset using a specific filtered MSE loss function. Experiment results demonstrate significant improvement of calculation speed and reduction of computational overheads compared to the detailed numerical methods. In addition, the proposed model has shown excellent modeling fidelity across various input frequencies using variable step solvers.