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

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 Abstract: 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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Language(s): eng - English
 Dates: 2021-12-302021-12-30
 Publication Status: Finally published
 Pages: 16
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: 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
 Degree: -

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Title: IEEE Access
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 10 Sequence Number: - Start / End Page: 1289 - 1304 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/1803142
Publisher: Institute of Electrical and Electronics Engineers (IEEE)