IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v340y2023ics030626192300377x.html
   My bibliography  Save this article

Detection and classification of DC and feeder faults in DC microgrid using new morphological operators with multi class AdaBoost algorithm

Author

Listed:
  • Dash, P.K.
  • Rekha Pattnaik, Smruti
  • N.V.D.V. Prasad, Eluri
  • Bisoi, Ranjeeta

Abstract

DC microgrids with energy storage systems based on photovoltaic (PV) and wind energy are gaining popularity as a means to offer users with reliable supply in either a stand-alone or grid connected mode. However, because DC and AC side faults have similar current–voltage profiles, developing a viable safety strategy for the proposed integrated DC microgrid is difficult. Traditional protection techniques based on pre-defined thresholds are unable to discriminate between DC and AC side faults, and so fail to offer independent control actions in both circumstances. In this context, new morphological operators with improved AdaBoost algorithm is proposed for detecting and classifying the AC and DC side faults in the proposed DC microgrid. To explore this, current signals are captured at the DC bus of the proposed integrated DC microgrid. The captured signals comprise background noise which is eliminated by dilation erosion difference operator (DEDO) and opening closing difference (OCDO) operators. The two operators work together to meet the accurate fault detection to avoid nuisance tripping by multiscale operation of structuring element (SE). For effective outcomes the multiple scales are optimized by sparse kurtosis (SK) index. The optimized scales are passes through target features to retrieve the data. The acquired data is sent into the multi-class AdaBoost approach, which recognizes faults by modifying the distribution of data and iteratively adjusting the weight of each instance. The proposed system's efficacy is tested using the MATLAB/Simulink platform under various operating situations such as load variation, irradiation and fault resistance changes. The proposed algorithm's superiority is demonstrated by comparing it to existing approaches using confusion matrix (CM) parameters.

Suggested Citation

  • Dash, P.K. & Rekha Pattnaik, Smruti & N.V.D.V. Prasad, Eluri & Bisoi, Ranjeeta, 2023. "Detection and classification of DC and feeder faults in DC microgrid using new morphological operators with multi class AdaBoost algorithm," Applied Energy, Elsevier, vol. 340(C).
  • Handle: RePEc:eee:appene:v:340:y:2023:i:c:s030626192300377x
    DOI: 10.1016/j.apenergy.2023.121013
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030626192300377X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2023.121013?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Monadi, Mehdi & Amin Zamani, M. & Ignacio Candela, Jose & Luna, Alvaro & Rodriguez, Pedro, 2015. "Protection of AC and DC distribution systems Embedding distributed energy resources: A comparative review and analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1578-1593.
    2. 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.
    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. Zhao, Yingying & Zhao, Chen & Li, Haibin & Ren, Jiwei & Zhou, Shuxing & Zhao, Yiying, 2024. "New member of micro power sources for extreme environmental explorations: X-ray-voltaic batteries," Applied Energy, Elsevier, vol. 353(PB).

    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. Mishra, Manohar & Patnaik, Bhaskar & Biswal, Monalisa & Hasan, Shazia & Bansal, Ramesh C., 2022. "A systematic review on DC-microgrid protection and grounding techniques: Issues, challenges and future perspective," Applied Energy, Elsevier, vol. 313(C).
    2. Ahmad Alzahrani & Pourya Shamsi & Mehdi Ferdowsi, 2020. "Interleaved Multistage Step-Up Topologies with Voltage Multiplier Cells," Energies, MDPI, vol. 13(22), pages 1-18, November.
    3. Bui, Duong Minh & Chen, Shi-Lin & Lien, Keng-Yu & Chang, Yung-Ruei & Lee, Yih-Der & Jiang, Jheng-Lun, 2017. "Investigation on transient behaviours of a uni-grounded low-voltage AC microgrid and evaluation on its available fault protection methods: Review and proposals," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 1417-1452.
    4. Krzysztof Barbusiński & Paweł Kwaśnicki & Anna Gronba-Chyła & Agnieszka Generowicz & Józef Ciuła & Bartosz Szeląg & Francesco Fatone & Agnieszka Makara & Zygmunt Kowalski, 2024. "Influence of Environmental Conditions on the Electrical Parameters of Side Connectors in Glass–Glass Photovoltaic Modules," Energies, MDPI, vol. 17(3), pages 1-13, January.
    5. Arcia-Garibaldi, Guadalupe & Cruz-Romero, Pedro & Gómez-Expósito, Antonio, 2018. "Future power transmission: Visions, technologies and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 285-301.
    6. Jayamaha, D.K.J.S. & Lidula, N.W.A. & Rajapakse, A.D., 2020. "Protection and grounding methods in DC microgrids: Comprehensive review and analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    7. Monadi, Mehdi & Zamani, M. Amin & Koch-Ciobotaru, Cosmin & Candela, Jose Ignacio & Rodriguez, Pedro, 2016. "A communication-assisted protection scheme for direct-current distribution networks," Energy, Elsevier, vol. 109(C), pages 578-591.
    8. Li, B. & Delpha, C. & Diallo, D. & Migan-Dubois, A., 2021. "Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    9. Peng Tian & Zetao Li & Zhenghang Hao, 2019. "A Doubly-Fed Induction Generator Adaptive Control Strategy and Coordination Technology Compatible with Feeder Automation," Energies, MDPI, vol. 12(23), pages 1-21, November.
    10. Dorotea Dimitrova Angelova & Diego Carmona Fernández & Manuel Calderón Godoy & Juan Antonio Álvarez Moreno & Juan Félix González González, 2024. "A Review on Digital Twins and Its Application in the Modeling of Photovoltaic Installations," Energies, MDPI, vol. 17(5), pages 1-29, March.
    11. Rachel M. Emerson & Nepu Saha & Pralhad H. Burli & Jordan L. Klinger & Tiasha Bhattacharjee & Lorenzo Vega-Montoto, 2024. "Analyzing Potential Failures and Effects in a Pilot-Scale Biomass Preprocessing Facility for Improved Reliability," Energies, MDPI, vol. 17(11), pages 1-20, May.
    12. Wang, Gang & Zhang, Zhen & Chen, Zeshao, 2023. "Design and performance evaluation of a novel CPV-T system using nano-fluid spectrum filter and with high solar concentrating uniformity," Energy, Elsevier, vol. 267(C).
    13. Wajahat Ullah Khan Tareen & Muhammad Aamir & Saad Mekhilef & Mutsuo Nakaoka & Mehdi Seyedmahmoudian & Ben Horan & Mudasir Ahmed Memon & Nauman Anwar Baig, 2018. "Mitigation of Power Quality Issues Due to High Penetration of Renewable Energy Sources in Electric Grid Systems Using Three-Phase APF/STATCOM Technologies: A Review," Energies, MDPI, vol. 11(6), pages 1-41, June.
    14. Bakdi, Azzeddine & Bounoua, Wahiba & Mekhilef, Saad & Halabi, Laith M., 2019. "Nonparametric Kullback-divergence-PCA for intelligent mismatch detection and power quality monitoring in grid-connected rooftop PV," Energy, Elsevier, vol. 189(C).
    15. Fouzi Harrou & Bilal Taghezouit & Sofiane Khadraoui & Abdelkader Dairi & Ying Sun & Amar Hadj Arab, 2022. "Ensemble Learning Techniques-Based Monitoring Charts for Fault Detection in Photovoltaic Systems," Energies, MDPI, vol. 15(18), pages 1-28, September.
    16. Emmanuel, Michael & Rayudu, Ramesh, 2017. "Evolution of dispatchable photovoltaic system integration with the electric power network for smart grid applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 207-224.
    17. Nien-Che Yang & Harun Ismail, 2022. "Voting-Based Ensemble Learning Algorithm for Fault Detection in Photovoltaic Systems under Different Weather Conditions," Mathematics, MDPI, vol. 10(2), pages 1-18, January.
    18. Srivastava, Chetan & Tripathy, Manoj, 2021. "DC microgrid protection issues and schemes: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    19. Jirada Gosumbonggot & Goro Fujita, 2019. "Global Maximum Power Point Tracking under Shading Condition and Hotspot Detection Algorithms for Photovoltaic Systems," Energies, MDPI, vol. 12(5), pages 1-23, March.
    20. Rico Espinosa, Alejandro & Bressan, Michael & Giraldo, Luis Felipe, 2020. "Failure signature classification in solar photovoltaic plants using RGB images and convolutional neural networks," Renewable Energy, Elsevier, vol. 162(C), pages 249-256.

    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:eee:appene:v:340:y:2023:i:c:s030626192300377x. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.