IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i6p969-d213440.html
   My bibliography  Save this article

FPGA Based Real-Time Emulation System for Power Electronics Converters

Author

Listed:
  • Jaka Marguč

    (Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, Slovenia)

  • Mitja Truntič

    (Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, Slovenia)

  • Miran Rodič

    (Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, Slovenia)

  • Miro Milanovič

    (Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, Slovenia)

Abstract

This paper deals with an emulation system for Power Electronics Converters (PEC). The emulation of PECs is performed on a Field-Programmable Gate Array (FPGA) capable of hard real-time operation. To obtain such a system, the converter operation is described using a differential equations-based model designed with the graph theory. Differential equation coefficients are changed according to the type of converter and pulse-width modulation (PWM) signals. The tie-set and incidence matrix approach for the converter modelling is performed to describe the converter operation in a general way. Such approach enables that any type of PECs can be described appropriately. The emulator was verified experimentally by synchronous operation with a real DC-AC converter built for this purposes.

Suggested Citation

  • Jaka Marguč & Mitja Truntič & Miran Rodič & Miro Milanovič, 2019. "FPGA Based Real-Time Emulation System for Power Electronics Converters," Energies, MDPI, vol. 12(6), pages 1-23, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:969-:d:213440
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/6/969/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/6/969/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Erfan Mohagheghi & Aouss Gabash & Pu Li, 2017. "A Framework for Real-Time Optimal Power Flow under Wind Energy Penetration," Energies, MDPI, vol. 10(4), pages 1-28, April.
    2. Fumin Ma & Gregory M. P. O’Hare & Tengfei Zhang & Michael J. O’Grady, 2015. "Model Property Based Material Balance and Energy Conservation Analysis for Process Industry Energy Transfer Systems," Energies, MDPI, vol. 8(10), pages 1-21, October.
    3. Mahmoud Saleh & Yusef Esa & Ahmed Mohamed, 2018. "Applications of Complex Network Analysis in Electric Power Systems," Energies, MDPI, vol. 11(6), pages 1-16, May.
    4. Mohagheghi, Erfan & Gabash, Aouss & Alramlawi, Mansour & Li, Pu, 2018. "Real-time optimal power flow with reactive power dispatch of wind stations using a reconciliation algorithm," Renewable Energy, Elsevier, vol. 126(C), pages 509-523.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rui Qin & Chunhua Yang & Hongwei Tao & Tao Peng & Chao Yang & Zhiwen Chen, 2019. "A Power Loss Decrease Method Based on Finite Set Model Predictive Control for a Motor Emulator with Reduced Switch Count," Energies, MDPI, vol. 12(24), pages 1-25, December.
    2. Remigiusz Wisniewski, 2021. "Design of Petri Net-Based Cyber-Physical Systems Oriented on the Implementation in Field Programmable Gate Arrays," Energies, MDPI, vol. 14(21), pages 1-25, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Erfan Mohagheghi & Mansour Alramlawi & Aouss Gabash & Pu Li, 2018. "A Survey of Real-Time Optimal Power Flow," Energies, MDPI, vol. 11(11), pages 1-20, November.
    2. Zhang, Wenyu & Zhang, Lifang & Wang, Jianzhou & Niu, Xinsong, 2020. "Hybrid system based on a multi-objective optimization and kernel approximation for multi-scale wind speed forecasting," Applied Energy, Elsevier, vol. 277(C).
    3. Kumar, Abhishek & Meena, Nand K. & Singh, Arvind R. & Deng, Yan & He, Xiangning & Bansal, R.C. & Kumar, Praveen, 2019. "Strategic integration of battery energy storage systems with the provision of distributed ancillary services in active distribution systems," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    4. Li, Zhengmao & Xu, Yan, 2019. "Temporally-coordinated optimal operation of a multi-energy microgrid under diverse uncertainties," Applied Energy, Elsevier, vol. 240(C), pages 719-729.
    5. Erfan Mohagheghi & Mansour Alramlawi & Aouss Gabash & Frede Blaabjerg & Pu Li, 2020. "Real-Time Active-Reactive Optimal Power Flow with Flexible Operation of Battery Storage Systems," Energies, MDPI, vol. 13(7), pages 1-17, April.
    6. Mohagheghi, Erfan & Gabash, Aouss & Alramlawi, Mansour & Li, Pu, 2018. "Real-time optimal power flow with reactive power dispatch of wind stations using a reconciliation algorithm," Renewable Energy, Elsevier, vol. 126(C), pages 509-523.
    7. Tio, Adonis E. & Hill, David J. & Ma, Jin, 2020. "Can graph properties determine future grid adequacy for power injection diversity?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    8. Cailian Gu & Yibo Wang & Weisheng Wang & Yang Gao, 2023. "Research on Load State Sensing and Early Warning Method of Distribution Network under High Penetration Distributed Generation Access," Energies, MDPI, vol. 16(7), pages 1-15, March.
    9. Shaohui Li & Xuejin Sun & Riwei Zhang & Chuanliang Zhang, 2019. "A Feasibility Study of Simulating the Micro-Scale Wind Field for Wind Energy Applications by NWP/CFD Model with Improved Coupling Method and Data Assimilation," Energies, MDPI, vol. 12(13), pages 1-19, July.
    10. Ding, Jie & Xu, Yujie & Chen, Haisheng & Sun, Wenwen & Hu, Shan & Sun, Shuang, 2019. "Value and economic estimation model for grid-scale energy storage in monopoly power markets," Applied Energy, Elsevier, vol. 240(C), pages 986-1002.
    11. Chen, Wenhao & Li, Jichao & Jiang, Jiang & Chen, Gang, 2022. "Weighted interdependent network disintegration strategy based on Q-learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    12. Zheng, Xiaoyuan & Kang, Yu & Li, Hongyi & Li, Jitao, 2023. "Multiple description encoding-decoding-based resilient filtering for complex networks under the round-Robin protocol," Applied Mathematics and Computation, Elsevier, vol. 458(C).
    13. Forsberg, Samuel & Thomas, Karin & Bergkvist, Mikael, 2023. "Power grid vulnerability analysis using complex network theory: A topological study of the Nordic transmission grid," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    14. Sirote Khunkitti & Apirat Siritaratiwat & Suttichai Premrudeepreechacharn, 2021. "Multi-Objective Optimal Power Flow Problems Based on Slime Mould Algorithm," Sustainability, MDPI, vol. 13(13), pages 1-21, July.
    15. Wang, Wensheng & Karimi, Faezeh & Khalilpour, Kaveh & Green, David & Varvarigos, Manos, 2023. "Robustness analysis of electricity networks against failure or attack: The case of the Australian National Electricity Market (NEM)," International Journal of Critical Infrastructure Protection, Elsevier, vol. 41(C).
    16. Mahmoud Saleh & Yusef Esa & Mohamed El Hariri & Ahmed Mohamed, 2019. "Impact of Information and Communication Technology Limitations on Microgrid Operation," Energies, MDPI, vol. 12(15), pages 1-24, July.
    17. Laura Cornejo-Bueno & Lucas Cuadra & Silvia Jiménez-Fernández & Javier Acevedo-Rodríguez & Luis Prieto & Sancho Salcedo-Sanz, 2017. "Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data," Energies, MDPI, vol. 10(11), pages 1-27, November.
    18. Ding, Dong & Tang, Ze & Wang, Yan & Ji, Zhicheng, 2020. "Synchronization of nonlinearly coupled complex networks: Distributed impulsive method," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
    19. Lenin Kanagasabai, 2022. "Real Power loss reduction by hybrid pan troglodytes optimization: extreme learning machine based augmented sine: cosine algorithms," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1102-1120, June.
    20. Li, Junjun & Yu, Anqi & Xu, Bowei, 2022. "Risk propagation and evolution analysis of multi-level handlings at automated terminals based on double-layer dynamic network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:969-:d:213440. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.