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A fast and accurate grid impedance measurement of three-phase power systemsis crucial for online assessment of power system stability and adaptive controlof grid-connected converters. Existing grid impedance measurement approachestypically rely on pointwise sinusoidal injections or sequential widebandperturbations to identify a nonparametric grid impedance curve via fast Fouriercomputations in the frequency domain. This is not only time-consuming, but alsoinaccurate during time-varying grid conditions, while on top of that, theidentified nonparametric model cannot be immediately used for stabilityanalysis or control design. To tackle these problems, we propose to useparametric system identification techniques (e.g., prediction error or subspacemethods) to obtain a parametric impedance model directly from time-domaincurrent and voltage data. Our approach relies on injecting wideband excitationsignals in the converter's controller and allows to accurately identify thegrid impedance in closed loop within one injection and measurement cycle. Eventhough the underlying parametric system identification techniques arewell-studied in general, their utilization in a grid impedance identificationsetup poses specific challenges, is vastly underexplored, and has not gainedadequate attention in urgent and timely power systems applications. To thisend, we demonstrate in numerical experiments how the proposed parametricapproach can accomplish a significant improvement compared to prevalentnonparametric methods.

MIMO Grid Impedance Identification of Three-Phase Power Systems: Parametric vs. Nonparametric Approaches