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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
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    References listed on IDEAS

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    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).
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