A data-driven identification method for impedance stability analysis of inverter-based resources

Obtaining inverter controller information may be a premise for seeking its dynamic behaviour. But accurate knowledge of such information would be unrealistic for real functioning inverter-interfaced generators (IIGs), which hinders the stability analysis of the IIG. A new data-driven impedance identification method is proposed for stability analysis, which involves an improved sparse identification algorithm as an ancillary function within the system identification framework. It contains mainly two design stages. First, the transform basis matrix (TBM) is devised systematically as a prior knowledge library to contain the possibly existing control structures. In the second stage, a sparse identification algorithm is reformulated in order to extract the relevant structures in TBM while obtaining controller parameters. The authors demonstrate that the sparse vector between the TBM and output signal is closely related to the controller structure. The effectiveness of the proposed method is verified on grid-connected inverters based on droop control and virtual synchronous machine control. This work reports a data-driven impedance identification method for the inverter-based resources. The compressed sensing technique is applied to inverter parameter identification. As a dynamic library induces the proposed machine learning algorithm, the proposed identification only needs less data and fast training. image