IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v16y2024i11p428-d1525189.html
   My bibliography  Save this article

Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach

Author

Listed:
  • David Carrascal

    (Universidad de Alcalá, Departamento de Automática, Escuela Politécnica Superior, 28805 Alcalá de Henares, Spain
    These authors contributed equally to this work.)

  • Paula Bartolomé

    (Universidad de Alcalá, Departamento de Automática, Escuela Politécnica Superior, 28805 Alcalá de Henares, Spain
    These authors contributed equally to this work.)

  • Elisa Rojas

    (Universidad de Alcalá, Departamento de Automática, Escuela Politécnica Superior, 28805 Alcalá de Henares, Spain)

  • Diego Lopez-Pajares

    (Universidad de Alcalá, Departamento de Automática, Escuela Politécnica Superior, 28805 Alcalá de Henares, Spain)

  • Nicolas Manso

    (Universidad de Alcalá, Departamento de Automática, Escuela Politécnica Superior, 28805 Alcalá de Henares, Spain)

  • Javier Diaz-Fuentes

    (Universidad de Alcalá, Departamento de Automática, Escuela Politécnica Superior, 28805 Alcalá de Henares, Spain)

Abstract

Smart grids (SGs) are essential for the efficient and distributed management of electrical distribution networks. A key task in SG management is fault detection and subsequently, network reconfiguration to minimize power losses and balance loads. This process should minimize power losses while optimizing distribution by balancing loads across the grid. However, the current literature yields a lack of methods for efficient fault prediction and fast reconfiguration. To achieve this goal, this paper builds on DEN2DE, an adaptable routing and reconfiguration solution potentially applicable to SGs, and investigates its potential extension with AI-based fault prediction using real-world datasets and randomly generated topologies based on the IEEE 123 Node Test Feeder. The study applies models based on Machine Learning (ML) and Deep Learning (DL) techniques, specifically evaluating Random Forest (RF) and Support Vector Machine (SVM) as ML methods, and Artificial Neural Network (ANN) as a DL method, evaluating each for accuracy, precision, and recall. Results indicate that the RF model with Recursive Feature Elimination (RFECV) achieves 94.28% precision and 81.05% recall, surpassing SVM (precision 89.32%, recall 6.95%) and ANN (precision 72.17%, recall 13.49%) in fault detection accuracy and reliability.

Suggested Citation

  • David Carrascal & Paula Bartolomé & Elisa Rojas & Diego Lopez-Pajares & Nicolas Manso & Javier Diaz-Fuentes, 2024. "Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach," Future Internet, MDPI, vol. 16(11), pages 1-26, November.
  • Handle: RePEc:gam:jftint:v:16:y:2024:i:11:p:428-:d:1525189
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/16/11/428/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/16/11/428/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ding, Tao & Lin, Yanling & Bie, Zhaohong & Chen, Chen, 2017. "A resilient microgrid formation strategy for load restoration considering master-slave distributed generators and topology reconfiguration," Applied Energy, Elsevier, vol. 199(C), pages 205-216.
    2. Ahmed Sami Alhanaf & Hasan Huseyin Balik & Murtaza Farsadi, 2023. "Intelligent Fault Detection and Classification Schemes for Smart Grids Based on Deep Neural Networks," Energies, MDPI, vol. 16(22), pages 1-19, November.
    3. Marashi, Koosha & Sarvestani, Sahra Sedigh & Hurson, Ali R., 2021. "Identification of interdependencies and prediction of fault propagation for cyber–physical systems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    Full references (including those not matched with items on IDEAS)

    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. Younes Zahraoui & Tarmo Korõtko & Argo Rosin & Saad Mekhilef & Mehdi Seyedmahmoudian & Alex Stojcevski & Ibrahim Alhamrouni, 2024. "AI Applications to Enhance Resilience in Power Systems and Microgrids—A Review," Sustainability, MDPI, vol. 16(12), pages 1-35, June.
    2. Wang, Yi & Qiu, Dawei & Sun, Mingyang & Strbac, Goran & Gao, Zhiwei, 2023. "Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach," Applied Energy, Elsevier, vol. 335(C).
    3. Wang, Hongping & Fang, Yi-Ping & Zio, Enrico, 2022. "Resilience-oriented optimal post-disruption reconfiguration for coupled traffic-power systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    4. Alex Guamán & Alex Valenzuela, 2021. "Distribution Network Reconfiguration Applied to Multiple Faulty Branches Based on Spanning Tree and Genetic Algorithms," Energies, MDPI, vol. 14(20), pages 1-16, October.
    5. Yang, Chao & Yao, Wei & Fang, Jiakun & Ai, Xiaomeng & Chen, Zhe & Wen, Jinyu & He, Haibo, 2019. "Dynamic event-triggered robust secondary frequency control for islanded AC microgrid," Applied Energy, Elsevier, vol. 242(C), pages 821-836.
    6. 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.
    7. Abbasizadeh, Ali & Azad-Farsani, Ehsan, 2024. "Cyber-constrained load shedding for smart grid resilience enhancement," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    8. Shi, Qingxin & Li, Fangxing & Dong, Jin & Olama, Mohammed & Wang, Xiaofei & Winstead, Chris & Kuruganti, Teja, 2022. "Co-optimization of repairs and dynamic network reconfiguration for improved distribution system resilience," Applied Energy, Elsevier, vol. 318(C).
    9. Younesi, Abdollah & Shayeghi, Hossein & Wang, Zongjie & Siano, Pierluigi & Mehrizi-Sani, Ali & Safari, Amin, 2022. "Trends in modern power systems resilience: State-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    10. Yixiao Fang & Junjie Yang & Wei Jiang, 2023. "Optimal Scheduling Strategy of Microgrid Based on Reactive Power Compensation of Electric Vehicles," Energies, MDPI, vol. 16(22), pages 1-23, November.
    11. Hua Zhan & Changxu Jiang & Zhen Lin, 2024. "A Novel Graph Reinforcement Learning-Based Approach for Dynamic Reconfiguration of Active Distribution Networks with Integrated Renewable Energy," Energies, MDPI, vol. 17(24), pages 1-19, December.
    12. Mehrjerdi, Hasan & Hemmati, Reza, 2020. "Coordination of vehicle-to-home and renewable capacity resources for energy management in resilience and self-healing building," Renewable Energy, Elsevier, vol. 146(C), pages 568-579.
    13. 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.
    14. Habibollah Raoufi & Vahid Vahidinasab & Kamyar Mehran, 2020. "Power Systems Resilience Metrics: A Comprehensive Review of Challenges and Outlook," Sustainability, MDPI, vol. 12(22), pages 1-24, November.
    15. Ning Xin & Laijun Chen & Linrui Ma & Yang Si, 2022. "A Rolling Horizon Optimization Framework for Resilient Restoration of Active Distribution Systems," Energies, MDPI, vol. 15(9), pages 1-14, April.
    16. Mansouri, S.A. & Ahmarinejad, A. & Nematbakhsh, E. & Javadi, M.S. & Esmaeel Nezhad, A. & Catalão, J.P.S., 2022. "A sustainable framework for multi-microgrids energy management in automated distribution network by considering smart homes and high penetration of renewable energy resources," Energy, Elsevier, vol. 245(C).
    17. Mousavizadeh, Saeed & Haghifam, Mahmoud-Reza & Shariatkhah, Mohammad-Hossein, 2018. "A linear two-stage method for resiliency analysis in distribution systems considering renewable energy and demand response resources," Applied Energy, Elsevier, vol. 211(C), pages 443-460.
    18. Lee, J. & Razeghi, G. & Samuelsen, S., 2022. "Generic microgrid controller with self-healing capabilities," Applied Energy, Elsevier, vol. 308(C).
    19. Ghasemi, Sasan & Moshtagh, Jamal, 2022. "Distribution system restoration after extreme events considering distributed generators and static energy storage systems with mobile energy storage systems dispatch in transportation systems," Applied Energy, Elsevier, vol. 310(C).
    20. Li, Peng & Ji, Jie & Ji, Haoran & Song, Guanyu & Wang, Chengshan & Wu, Jianzhong, 2020. "Self-healing oriented supply restoration method based on the coordination of multiple SOPs in active distribution networks," Energy, Elsevier, vol. 195(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:jftint:v:16:y:2024:i:11:p:428-:d:1525189. 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.