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  Black-Box Impedance Prediction of Grid-Tied VSCs Under Variable Operating Conditions

Qiu, Q., Huang, Y., Ma, R., Kurths, J., Zhan, M. (2021): Black-Box Impedance Prediction of Grid-Tied VSCs Under Variable Operating Conditions. - IEEE Access, 10, 1289-1304.
https://doi.org/10.1109/ACCESS.2021.3139435

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Qiu, Qi 1, Autor
Huang, Yifan 1, Autor
Ma, Rui 1, Autor
Kurths, Jürgen2, Autor              
Zhan, Meng 1, Autor
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: Impedance/admittance models (IM/AMs) have been widely used to analyze the small-signal stability of grid-tied power electronic devices, such as the voltage source converter (VSC). They can be either derived from theoretical analysis under white-box conditions, where all parameters and control structures are fully known, or measured based on experiments under black-box conditions, where the topology and parameters of the controllers are completely unknown. As the IM/AMs depend on specific operating conditions, it is highly desirable to develop fast algorithms for IM/AM prediction (or estimation) under the black-box and variable-operating-point conditions. This article extends the nearly-decoupled AM method for sequence AMs proposed recently by Liu et al to fit any unknown control structure, including not only grid-following VSC, but also grid-forming VSC. It is, therefore, referred to as the fully-decoupled IM (FDIM) method. Furthermore, a curve fitting method for the transfer function is proposed to expedite the algorithm, based on the information of a few disturbance frequencies only. Finally, the algorithm is verified by wide simulations and experiments under different situations, including the direct-drive wind turbine generator. The whole approach is expected to be broadly applicable to the stability analysis of power-electronic-based power systems under variable operating conditions.

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Sprache(n): eng - Englisch
 Datum: 2021-12-302021-12-30
 Publikationsstatus: Final veröffentlicht
 Seiten: 16
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1109/ACCESS.2021.3139435
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Energy
Research topic keyword: Nonlinear Dynamics
Working Group: Network- and machine-learning-based prediction of extreme events
OATYPE: Gold Open Access
 Art des Abschluß: -

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Titel: IEEE Access
Genre der Quelle: Zeitschrift, SCI, Scopus, p3
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Seiten: - Band / Heft: 10 Artikelnummer: - Start- / Endseite: 1289 - 1304 Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/1803142
Publisher: Institute of Electrical and Electronics Engineers (IEEE)