Memory Compact and Computationally Efficient Methods for Modeling Saturation in Synchronous Machines

Synchronous machines are commonly used in power generation, electric propulsion, and industrial automation. Accurate modeling of these machines is essential, particularly in addressing challenges posed by nonlinear characteristics such as magnetic saturation. Traditional models often rely on computationally intensive methods to account for these nonlinearities, leading to slow simulations and high memory requirements. This paper introduces two new models: the enhanced explicit flux correction (EFC-2D) model, which captures magnetic saturation using two-dimensional lookup tables (2D-LUTs) with improved computational efficiency, and the single polynomial (S-POLY) model, which represents saturation using polynomials to significantly reduce memory usage. The proposed models are compared to the advanced one-dimensional explicit flux correct model (EFC-1D) and the model in Matlab/Simulink's Simscape Electrical Specialized Power Systems (SPS) toolbox. Simulation results, including both offline and real-time tests, show that EFC-2D and S-POLY models are computationally faster than conventional methods, with S-POLY being more memory-efficient than EFC-1D and EFC-2D models. These improvements make the proposed models ideal for high-performance simulations requiring both speed and memory optimization.