Data-Driven Modeling of Power-Electronics-Based Power System Considering the Operating Point Variation

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Abstrakt

Large-scale integrations of power-electronics devices have introduced the stability challenges to the conventional power system. The stability of the power-electronics-based power systems, which are modeled by a Multi-Input Multi-Output (MIMO) transfer function matrix, can be analyzed based on the Nyquist Criterion. However, since no or limited information about the internal control details, this matrix can only be obtained using the measured data. On the other hand, the elements of the matrix will change along with the operating point of each power electronics converter, which introduces the challenge to guarantee the interaction stability of each inverter at different operating points. In this paper, a data-driven method is proposed to overcome this operating-point dependent challenge. An artificial neural network (ANN) is used to characterize the operating-point dependent model of power-electronics-based power systems. The comparison results confirm the accuracy of the impedance model obtained by this data-driven modeling method.
OriginalsprogEngelsk
Titel2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings
Antal sider5
ForlagIEEE
Publikationsdato2021
Sider3513-3517
ISBN (Elektronisk)9781728151359
DOI
StatusUdgivet - 2021
Begivenhed13th IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Virtual, Online, Canada
Varighed: 10 okt. 202114 okt. 2021

Konference

Konference13th IEEE Energy Conversion Congress and Exposition, ECCE 2021
Land/OmrådeCanada
ByVirtual, Online
Periode10/10/202114/10/2021
SponsorGMW Associates, IEEE Industry Applications Society (IAS), IEEE Power Electronics Society (PELS), Opal-RT Technologies, STMicroelectronics
NavnIEEE Energy Conversion Congress and Exposition
ISSN2329-3721

Bibliografisk note

Publisher Copyright:
© 2021 IEEE.

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