Data-driven Small-signal Modeling for Converter-based Power Systems
This work provides a modeling approach for power system engineers to analyze stability in grids with high renewable energy integration, but it is incremental as it applies existing regression methods to this domain.
The authors tackled the problem of modeling converter-based power systems for small-signal stability analysis by developing a data-driven procedure using Decision Tree and Spline regression, achieving performance comparisons in accuracy and computational time on test cases with high converter penetration.
This article details a complete procedure to derive a data-driven small-signal-based model useful to perform converter-based power system related studies. To compute the model, Decision Tree (DT) regression, both using single DT and ensemble DT, and Spline regression have been employed and their performances have been compared, in terms of accuracy, training and computing time. The methodology includes a comprehensive step-by-step procedure to develop the model: data generation by conventional simulation and mathematical models, databases (DBs) arrangement, regression training and testing, realizing prediction for new instances. The methodology has been developed using an essential network and then tested on a more complex system, to show the validity and usefulness of the suggested approach. Both power systems test cases have the essential characteristics of converter-based power systems, simulating high penetration of converter interfaced generation and the presence of HVDC links. Moreover, it is proposed how to represent in a visual manner the results of the small-signal stability analysis for a wide range of system operating conditions, exploiting DT regressions. Finally, the possible applications of the model are discussed, highlighting the potential of the developed model in further power system small-signal related studies.