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- W4367694009 abstract "A fast and accurate grid impedance measurement of three-phase power systems is crucial for online assessment of power system stability and adaptive control of grid-connected converters. Existing grid impedance measurement approaches typically rely on pointwise sinusoidal injections or sequential wideband perturbations to identify a nonparametric grid impedance curve via fast Fourier computations in the frequency domain. This is not only time-consuming, but also inaccurate during time-varying grid conditions, while on top of that, the identified nonparametric model cannot be immediately used for stability analysis or control design. To tackle these problems, we propose to use parametric system identification techniques (e.g., prediction error or subspace methods) to obtain a parametric impedance model directly from time-domain current and voltage data. Our approach relies on injecting wideband excitation signals in the converter's controller and allows to accurately identify the grid impedance in closed loop within one injection and measurement cycle. Even though the underlying parametric system identification techniques are well-studied in general, their utilization in a grid impedance identification setup poses specific challenges, is vastly underexplored, and has not gained adequate attention in urgent and timely power systems applications. To this end, we demonstrate in numerical experiments how the proposed parametric approach can accomplish a significant improvement compared to prevalent nonparametric methods." @default.
- W4367694009 created "2023-05-03" @default.
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- W4367694009 date "2023-04-29" @default.
- W4367694009 modified "2023-10-18" @default.
- W4367694009 title "MIMO Grid Impedance Identification of Three-Phase Power Systems: Parametric vs. Nonparametric Approaches" @default.
- W4367694009 doi "https://doi.org/10.48550/arxiv.2305.00192" @default.
- W4367694009 hasPublicationYear "2023" @default.
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