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International Conference on Neural Information Processing

ICONIP 2017: Neural Information Processing pp 277–286Cite as

Dynamic Phasor Modeling of a Hybrid AC/DC Microgrid

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10639))

Abstract

The dynamic phasor (DP) model of a hybrid AC/DC microgird is proposed in this paper. This hybrid microgrid consists of an interlinking converter (ILC), wind turbine (WT) system, photovoltaic (PV) system and energy storage (ES) system, and can operate under either grid-connected mode or autonomous mode. A comprehensive definition of the dynamic phasor is given to make the developed model more compact and the physical meaning more clear. The developed microgrid model is validated by comparing with a detailed model considering switch operations in Matlab/Simulink. By comparison, the DP model turns out to consume much less time and have a satisfactory accuracy under various disturbances.

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Acknowledgement

This work was supported in part by National Key R&D Plan (2016YFB0900601) and State Grid Technology Program (study on principle, design and control technology of new operation mode of micro-grid based on electric spring).

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Authors and Affiliations

  1. Shanghai Jiao Tong University, 800 Dongchuan RD. Minhang District, Shanghai, China

    Jin Xu, Keyou Wang & Guojie Li

Authors
  1. Jin Xu
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  2. Keyou Wang
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  3. Guojie Li
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Corresponding author

Correspondence to Keyou Wang .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Xu, J., Wang, K., Li, G. (2017). Dynamic Phasor Modeling of a Hybrid AC/DC Microgrid. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10639. Springer, Cham. https://doi.org/10.1007/978-3-319-70136-3_30

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  • DOI: https://doi.org/10.1007/978-3-319-70136-3_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70135-6

  • Online ISBN: 978-3-319-70136-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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