Parameterizing Nonlinear Behavior of Short Circuit Current with Inverter-Based Resources

The integration of renewable energy sources (wind and solar farms etc.), introduces inverter-based resources (IBRs) into the power grid. These resources exhibit unique and nonlinear fault current characteristics. This research aims to develop a simulation environment utilizing a steady-state short circuit solver to analyze and parameterize the short circuit response of a large-scale network with IBRs. The focus is on employing an improved data fitting technique to enhance accuracy. The effectiveness of the proposed solver is demonstrated on a 120 kV test system. A variety of fault scenarios are simulated to generate a comprehensive dataset, which is subsequently used to parameterize the nonlinear behavior of short circuit current induced by IBRs, utilizing higher order polynomial data fitting technique. The proposed solver, with the improved current limiting strategy, achieved a comprehensive dataset with 100% convergence for balanced faults and 99.97% for unbalanced faults Furthermore, the employment of a higher-order polynomial data fitting technique yields 96% more accurate on average compared to locally weighted scatterplot smoothing (LOWESS) data fitting methods proposed in literature. This comparison highlights the enhanced performance of the proposed methodology, showcasing significant improvements in both efficiency and accuracy.