Hybrid Modeling Approach Combining Analytical and Neural Ordinary Differential Equations for Accelerated Simulation of Grid-Tied Inverter

Fine solving time step is commonly required in the simulation of grid-tied inverters due to the fast inner-loop control and high-frequency switching behavior. However, the dynamics of the grid-tied inverters, especially in a large-scale renewable power farm study, are frequently governed by the slower outer-loop control. The mismatching between the small solving time step and the slow outer loop leads to time-consuming simulations when using conventional numerical methods. While a neural network (NN) can serve as a surrogate for the entire grid-tied inverter, modeling full behaviors still demands large and computationally intensive networks. To address this issue, this paper proposes a hybrid modeling approach for accelerated simulation of the grid-tie inverters. It combines analytical models and neural ordinary differential equations (ODEs). Neural ODE (NODE) is employed to solely represent the inner loop dynamics, eliminating deep or complex networks, while the other components outside the inner loop are retained and analytically represented. This hybridization combines the coarse-step capability of NN-based surrogates with the physical interpretability and efficiency of analytical models. Experimental results demonstrate that the proposed hybrid approach achieves high-fidelity predictions under various disturbances and grid faults while significantly reducing simulation time compared with the conventional methods.