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Artificial Intelligence-Based Protection for Smart Grids

Author

Listed:
  • Mostafa Bakkar

    (Department of Electrical Engineering, Universitat Politècnica de Catalunya (UPC), C. Colom 1, 08222 Terrassa, Spain)

  • Santiago Bogarra

    (Department of Electrical Engineering, Universitat Politècnica de Catalunya (UPC), C. Colom 1, 08222 Terrassa, Spain)

  • Felipe Córcoles

    (Department of Electrical Engineering, Universitat Politècnica de Catalunya (UPC), C. Colom 1, 08222 Terrassa, Spain)

  • Ahmed Aboelhassan

    (Key Laboratory of More Electric Aircraft Technology of Zhejiang Province, University of Nottingham Ningbo China, Ningbo 315100, China)

  • Shuo Wang

    (Key Laboratory of More Electric Aircraft Technology of Zhejiang Province, University of Nottingham Ningbo China, Ningbo 315100, China)

  • Javier Iglesias

    (ABB Power Grids Spain S.A.U., San Romualdo 13, 28037 Madrid, Spain)

Abstract

Lately, adequate protection strategies need to be developed when Microgrids (MGs) are connected to smart grids to prevent undesirable tripping. Conventional relay settings need to be adapted to changes in Distributed Generator (DG) penetrations or grid reconfigurations, which is a complicated task that can be solved efficiently using Artificial Intelligence (AI)-based protection. This paper compares and validates the difference between conventional protection (overcurrent and differential) strategies and a new strategy based on Artificial Neural Networks (ANNs), which have been shown as adequate protection, especially with reconfigurable smart grids. In addition, the limitations of the conventional protections are discussed. The AI protection is employed through the communication between all Protective Devices (PDs) in the grid, and a backup strategy that employs the communication among the PDs in the same line. This paper goes a step further to validate the protection strategies based on simulations using the MATLAB TM platform and experimental results using a scaled grid. The AI-based protection method gave the best solution as it can be adapted for different grids with high accuracy and faster response than conventional protection, and without the need to change the protection settings. The scaled grid was designed for the smart grid to advocate the behavior of the protection strategies experimentally for both conventional and AI-based protections.

Suggested Citation

  • Mostafa Bakkar & Santiago Bogarra & Felipe Córcoles & Ahmed Aboelhassan & Shuo Wang & Javier Iglesias, 2022. "Artificial Intelligence-Based Protection for Smart Grids," Energies, MDPI, vol. 15(13), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4933-:d:856468
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    References listed on IDEAS

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    1. Govind Sahay Yogee & Om Prakash Mahela & Kapil Dev Kansal & Baseem Khan & Rajendra Mahla & Hassan Haes Alhelou & Pierluigi Siano, 2020. "An Algorithm for Recognition of Fault Conditions in the Utility Grid with Renewable Energy Penetration," Energies, MDPI, vol. 13(9), pages 1-22, May.
    2. Mohamad Norshahrani & Hazlie Mokhlis & Ab. Halim Abu Bakar & Jasrul Jamani Jamian & Shivashankar Sukumar, 2017. "Progress on Protection Strategies to Mitigate the Impact of Renewable Distributed Generation on Distribution Systems," Energies, MDPI, vol. 10(11), pages 1-30, November.
    3. 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.
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    Cited by:

    1. Hubert Szczepaniuk & Edyta Karolina Szczepaniuk, 2022. "Applications of Artificial Intelligence Algorithms in the Energy Sector," Energies, MDPI, vol. 16(1), pages 1-24, December.
    2. Hamed Rezapour & Sadegh Jamali & Alireza Bahmanyar, 2023. "Review on Artificial Intelligence-Based Fault Location Methods in Power Distribution Networks," Energies, MDPI, vol. 16(12), pages 1-18, June.

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