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Dynamics Analysis Using Koopman Mode Decomposition of a Microgrid Including Virtual Synchronous Generator-Based Inverters

Author

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  • Yuko Hirase

    (Department of Electrical, Electronic and Communications Engineering, Toyo University, 2100 Kujirai, Kawagoe 350-8585, Japan)

  • Yuki Ohara

    (Department of Electrical, Electronic and Communications Engineering, Toyo University, 2100 Kujirai, Kawagoe 350-8585, Japan)

  • Naoya Matsuura

    (Department of Electrical, Electronic and Communications Engineering, Toyo University, 2100 Kujirai, Kawagoe 350-8585, Japan)

  • Takeaki Yamazaki

    (Department of Electrical, Electronic and Communications Engineering, Toyo University, 2100 Kujirai, Kawagoe 350-8585, Japan)

Abstract

In the field of microgrids (MGs), steady-state power imbalances and frequency/voltage fluctuations in the transient state have been gaining prominence owing to the advancing distributed energy resources (DERs) connected to MGs via grid-connected inverters. Because a stable, safe power supply and demand must be maintained, accurate analyses of power system dynamics are crucial. However, the natural frequency components present in the dynamics make analyses complex. The nonlinearity and confidentiality of grid-connected inverters also hinder controllability. The MG considered in this study consisted of a synchronous generator (the main power source) and multiple grid-connected inverters with storage batteries and virtual synchronous generator (VSG) control. Although smart inverter controls such as VSG contribute to system stabilization, they induce system nonlinearity. Therefore, Koopman mode decomposition (KMD) was utilized in this study for consideration as a future method of data-driven analysis of the measured frequencies and voltages, and a frequency response analysis of the power system dynamics was performed. The Koopman operator is a linear operator on an infinite dimensional space, whereas the original dynamics is a nonlinear map on a finite state space. In other words, the proposed method can precisely analyze all the dynamics of the power system, which involve the complex nonlinearities caused by VSGs.

Suggested Citation

  • Yuko Hirase & Yuki Ohara & Naoya Matsuura & Takeaki Yamazaki, 2021. "Dynamics Analysis Using Koopman Mode Decomposition of a Microgrid Including Virtual Synchronous Generator-Based Inverters," Energies, MDPI, vol. 14(15), pages 1-20, July.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4581-:d:603662
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    References listed on IDEAS

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    1. Saehong Park & Seunghyoung Ryu & Yohwan Choi & Jihyo Kim & Hongseok Kim, 2015. "Data-Driven Baseline Estimation of Residential Buildings for Demand Response," Energies, MDPI, vol. 8(9), pages 1-21, September.
    2. Juan Montoya & Ron Brandl & Keerthi Vishwanath & Jay Johnson & Rachid Darbali-Zamora & Adam Summers & Jun Hashimoto & Hiroshi Kikusato & Taha Selim Ustun & Nayeem Ninad & Estefan Apablaza-Arancibia & , 2020. "Advanced Laboratory Testing Methods Using Real-Time Simulation and Hardware-in-the-Loop Techniques: A Survey of Smart Grid International Research Facility Network Activities," Energies, MDPI, vol. 13(12), pages 1-38, June.
    3. Hirase, Yuko & Abe, Kensho & Sugimoto, Kazushige & Sakimoto, Kenichi & Bevrani, Hassan & Ise, Toshifumi, 2018. "A novel control approach for virtual synchronous generators to suppress frequency and voltage fluctuations in microgrids," Applied Energy, Elsevier, vol. 210(C), pages 699-710.
    4. Yuko Hirase & Kazusa Uezaki & Dai Orihara & Hiroshi Kikusato & Jun Hashimoto, 2021. "Characteristic Analysis and Indexing of Multimachine Transient Stabilization Using Virtual Synchronous Generator Control," Energies, MDPI, vol. 14(2), pages 1-23, January.
    5. Nassir Cassamo & Jan-Willem van Wingerden, 2020. "On the Potential of Reduced Order Models for Wind Farm Control: A Koopman Dynamic Mode Decomposition Approach," Energies, MDPI, vol. 13(24), pages 1-21, December.
    6. Songkai Liu & Ruoyuan Shi & Yuehua Huang & Xin Li & Zhenhua Li & Lingyun Wang & Dan Mao & Lihuang Liu & Siyang Liao & Menglin Zhang & Guanghui Yan & Lian Liu, 2021. "A Data-Driven and Data-Based Framework for Online Voltage Stability Assessment Using Partial Mutual Information and Iterated Random Forest," Energies, MDPI, vol. 14(3), pages 1-16, January.
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    1. Lintao Ren & Hui Guo & Zhenlan Dou & Fei Wang & Lijun Zhang, 2022. "Modeling and Analysis of the Harmonic Interaction between Grid-Connected Inverter Clusters and the Utility Grid," Energies, MDPI, vol. 15(10), pages 1-19, May.

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