Artificial Neural Network Based Identification of Multi-Operating-Point Impedance Model

Mengfan Zhang; Xiongfei Wang; Dongsheng Yang; Mads Graesboll Christensen

doi:10.1109/TPEL.2020.3012136

Artificial Neural Network Based Identification of Multi-Operating-Point Impedance Model

Mengfan Zhang, Xiongfei Wang, Dongsheng Yang, Mads Graesboll Christensen

Research output: Contribution to journal › Journal article › Research › peer-review

48 Citations (Scopus)

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Abstract

The black-box impedance model of voltage source inverters (VSIs) can be measured at their terminals without access to internal control details, which greatly facilitate the analysis of inverter-grid interactions. However, the impedance model of VSI is dependent on its operating point and can have different profiles when the operating point is changed. This letter proposes a method for identifying the impedance model of VSI under a wide range of operating points. The approach is based on the artificial neural network (ANN), where a general framework for applying the ANN to identify the VSI impedance is established. The effectiveness of the ANN-based method is validated with the analytical impedance models.

Original language	English
Article number	9151366
Journal	IEEE Transactions on Power Electronics
Volume	36
Issue number	2
Pages (from-to)	1231-1235
Number of pages	5
ISSN	0885-8993
DOIs	https://doi.org/10.1109/TPEL.2020.3012136
Publication status	Published - Feb 2021

Keywords

Artificial neural networks
impedance measurement
multiple operating points
voltage source inverter

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10.1109/TPEL.2020.3012136

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title = "Artificial Neural Network Based Identification of Multi-Operating-Point Impedance Model",

abstract = "The black-box impedance model of voltage source inverters (VSIs) can be measured at their terminals without access to internal control details, which greatly facilitate the analysis of inverter-grid interactions. However, the impedance model of VSI is dependent on its operating point and can have different profiles when the operating point is changed. This letter proposes a method for identifying the impedance model of VSI under a wide range of operating points. The approach is based on the artificial neural network (ANN), where a general framework for applying the ANN to identify the VSI impedance is established. The effectiveness of the ANN-based method is validated with the analytical impedance models. ",

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AU - Zhang, Mengfan

AU - Wang, Xiongfei

AU - Yang, Dongsheng

AU - Christensen, Mads Graesboll

PY - 2021/2

Y1 - 2021/2

N2 - The black-box impedance model of voltage source inverters (VSIs) can be measured at their terminals without access to internal control details, which greatly facilitate the analysis of inverter-grid interactions. However, the impedance model of VSI is dependent on its operating point and can have different profiles when the operating point is changed. This letter proposes a method for identifying the impedance model of VSI under a wide range of operating points. The approach is based on the artificial neural network (ANN), where a general framework for applying the ANN to identify the VSI impedance is established. The effectiveness of the ANN-based method is validated with the analytical impedance models.

AB - The black-box impedance model of voltage source inverters (VSIs) can be measured at their terminals without access to internal control details, which greatly facilitate the analysis of inverter-grid interactions. However, the impedance model of VSI is dependent on its operating point and can have different profiles when the operating point is changed. This letter proposes a method for identifying the impedance model of VSI under a wide range of operating points. The approach is based on the artificial neural network (ANN), where a general framework for applying the ANN to identify the VSI impedance is established. The effectiveness of the ANN-based method is validated with the analytical impedance models.

KW - Artificial neural networks

KW - impedance measurement

KW - multiple operating points

KW - voltage source inverter

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DO - 10.1109/TPEL.2020.3012136

M3 - Journal article

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JO - IEEE Transactions on Power Electronics

JF - IEEE Transactions on Power Electronics

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ER -

Artificial Neural Network Based Identification of Multi-Operating-Point Impedance Model

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