IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i6p1586-d516110.html
   My bibliography  Save this article

Higher Order Sliding Mode Observer-Based Sensor Fault Detection in DC Microgrid’s Buck Converter

Author

Listed:
  • Daijiry Narzary

    (School of Electronics Engineering, Kyungpook National University, Daegu 41566, Korea)

  • Kalyana C. Veluvolu

    (School of Electronics Engineering, Kyungpook National University, Daegu 41566, Korea)

Abstract

Fault detection in a Direct Current (DC) microgrid with multiple interconnections of distributed generation units (DGUs) is an interesting topic of research. The occurrence of any sensor fault in the DC microgrid should be detected immediately by the fault detection network to achieve an overall stable performance of the system. This work focuses on sensor fault diagnosis of voltage and current sensors in interconnected DGUs of the microgrid. Two separate higher order sliding mode observers (HOSM) based on model dynamics are designed to estimate the voltage and current and generate the residuals for detecting the faulty sensors in DGUs. Multiplicative single and multiple sensor faults are considered in voltage and current sensors. By appropriate selection of threshold, single and multiple sensor fault detection strategies are formulated. A hierarchical controller is designed to ensure equal sharing of current among the DGUs of the DC microgrid and stabilize the system. Simulations are performed to validate the proposed approach for various configurations of the DC microgrid under various load and off noise conditions.

Suggested Citation

  • Daijiry Narzary & Kalyana C. Veluvolu, 2021. "Higher Order Sliding Mode Observer-Based Sensor Fault Detection in DC Microgrid’s Buck Converter," Energies, MDPI, vol. 14(6), pages 1-14, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:6:p:1586-:d:516110
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/6/1586/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/6/1586/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiaodong Chang & Jinquan Huang & Feng Lu, 2019. "Sensor Fault Tolerant Control for Aircraft Engines Using Sliding Mode Observer," Energies, MDPI, vol. 12(21), pages 1-15, October.
    2. Hare, James & Shi, Xiaofang & Gupta, Shalabh & Bazzi, Ali, 2016. "Fault diagnostics in smart micro-grids: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1114-1124.
    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. Mehdi Hosseinzadeh, 2022. "Fault-Tolerant Control for Microgrids—Recent Developments and Future Directions," Energies, MDPI, vol. 15(22), pages 1-5, November.

    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. Chen, Yizhong & He, Li & Li, Jing, 2017. "Stochastic dominant-subordinate-interactive scheduling optimization for interconnected microgrids with considering wind-photovoltaic-based distributed generations under uncertainty," Energy, Elsevier, vol. 130(C), pages 581-598.
    2. El-Sharafy, M. Zaki & Farag, Hany E.Z., 2017. "Back-feed power restoration using distributed constraint optimization in smart distribution grids clustered into microgrids," Applied Energy, Elsevier, vol. 206(C), pages 1102-1117.
    3. Kara Mostefa Khelil, Chérifa & Amrouche, Badia & Benyoucef, Abou soufiane & Kara, Kamel & Chouder, Aissa, 2020. "New Intelligent Fault Diagnosis (IFD) approach for grid-connected photovoltaic systems," Energy, Elsevier, vol. 211(C).
    4. Harrou, Fouzi & Sun, Ying & Taghezouit, Bilal & Saidi, Ahmed & Hamlati, Mohamed-Elkarim, 2018. "Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches," Renewable Energy, Elsevier, vol. 116(PA), pages 22-37.
    5. Syed Ali Abbas Kazmi & Muhammad Khuram Shahzad & Akif Zia Khan & Dong Ryeol Shin, 2017. "Smart Distribution Networks: A Review of Modern Distribution Concepts from a Planning Perspective," Energies, MDPI, vol. 10(4), pages 1-47, April.
    6. Andrés A. Zúñiga & Alexandre Baleia & João Fernandes & Paulo Jose Da Costa Branco, 2020. "Classical Failure Modes and Effects Analysis in the Context of Smart Grid Cyber-Physical Systems," Energies, MDPI, vol. 13(5), pages 1-26, March.
    7. Teke Gush & Syed Basit Ali Bukhari & Khawaja Khalid Mehmood & Samuel Admasie & Ji-Soo Kim & Chul-Hwan Kim, 2019. "Intelligent Fault Classification and Location Identification Method for Microgrids Using Discrete Orthonormal Stockwell Transform-Based Optimized Multi-Kernel Extreme Learning Machine," Energies, MDPI, vol. 12(23), pages 1-16, November.
    8. Giovanni Betta & Domenico Capriglione & Luigi Ferrigno & Marco Laracca & Gianfranco Miele & Nello Polese & Silvia Sangiovanni, 2021. "A Fault Diagnostic Scheme for Predictive Maintenance of AC/DC Converters in MV/LV Substations," Energies, MDPI, vol. 14(22), pages 1-23, November.
    9. Das, Saborni & Hazra, Abhik & Basu, Mousumi, 2018. "Metaheuristic optimization based fault diagnosis strategy for solar photovoltaic systems under non-uniform irradiance," Renewable Energy, Elsevier, vol. 118(C), pages 452-467.
    10. Nosratabadi, Seyyed Mostafa & Hooshmand, Rahmat-Allah & Gholipour, Eskandar, 2017. "A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 341-363.
    11. Noor Hussain & Mashood Nasir & Juan Carlos Vasquez & Josep M. Guerrero, 2020. "Recent Developments and Challenges on AC Microgrids Fault Detection and Protection Systems–A Review," Energies, MDPI, vol. 13(9), pages 1-31, May.
    12. Pillai, Dhanup S. & Rajasekar, N., 2018. "A comprehensive review on protection challenges and fault diagnosis in PV systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 18-40.
    13. Marzal, Silvia & Salas, Robert & González-Medina, Raúl & Garcerá, Gabriel & Figueres, Emilio, 2018. "Current challenges and future trends in the field of communication architectures for microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3610-3622.
    14. Shahriar Rahman Fahim & Subrata K. Sarker & S. M. Muyeen & Md. Rafiqul Islam Sheikh & Sajal K. Das, 2020. "Microgrid Fault Detection and Classification: Machine Learning Based Approach, Comparison, and Reviews," Energies, MDPI, vol. 13(13), pages 1-22, July.
    15. Marqusee, Jeffrey & Ericson, Sean & Jenket, Don, 2021. "Impact of emergency diesel generator reliability on microgrids and building-tied systems," Applied Energy, Elsevier, vol. 285(C).
    16. Joshuva Arockia Dhanraj & Ali Mostafaeipour & Karthikeyan Velmurugan & Kuaanan Techato & Prem Kumar Chaurasiya & Jenoris Muthiya Solomon & Anitha Gopalan & Khamphe Phoungthong, 2021. "An Effective Evaluation on Fault Detection in Solar Panels," Energies, MDPI, vol. 14(22), pages 1-14, November.
    17. Martha Lucia Orozco-Gutierrez, 2020. "An Interval-Arithmetic-Based Approach to the Parametric Identification of the Single-Diode Model of Photovoltaic Generators," Energies, MDPI, vol. 13(4), pages 1-22, February.
    18. Patnaik, Bhaskar & Mishra, Manohar & Bansal, Ramesh C. & Jena, Ranjan Kumar, 2020. "AC microgrid protection – A review: Current and future prospective," Applied Energy, Elsevier, vol. 271(C).
    19. Xiaojie Qiu & Xiaodong Chang & Jie Chen & Baiqing Fan, 2022. "Research on the Analytical Redundancy Method for the Control System of Variable Cycle Engine," Sustainability, MDPI, vol. 14(10), pages 1-11, May.
    20. Rizeakos, V. & Bachoumis, A. & Andriopoulos, N. & Birbas, M. & Birbas, A., 2023. "Deep learning-based application for fault location identification and type classification in active distribution grids," Applied Energy, Elsevier, vol. 338(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:14:y:2021:i:6:p:1586-:d:516110. 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.