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Explainable AI-Driven Quantum Deep Neural Network for Fault Location in DC Microgrids

Author

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  • Amir Hossein Poursaeed

    (Department of Electrical Engineering, Faculty of Engineering, Lorestan University, Khorram Abad 68151-44316, Iran)

  • Farhad Namdari

    (Department of Engineering, Faculty of Environment, Science, and Economy, University of Exeter, Exeter EX4 4QF, UK)

Abstract

Fault location in DC microgrids (DCMGs) is a critical challenge due to the system’s inherent complexities and the demand for high reliability in modern power systems. This study proposes an explainable artificial intelligence (XAI)-based quantum deep neural network (QDNN) framework to address fault localization challenges in DCMGs. First, voltage signals from the DCMG are collected and analyzed using high-order synchrosqueezing transform to detect traveling waves (TWs) and extract critical fault parameters such as time of arrival, magnitude, and polarity of the first and second TWs. These features are fed into the proposed QDNN model that integrates advanced learning techniques for accurate fault localization. The cumulative distance from the fault point to the bus connecting the DCMG to the power network is considered the output vector. The model uses a combination of deep learning and quantum computing techniques to extract features and improve accuracy. To ensure transparency, an XAI technique called Shapley additive explanations (SHAP) is applied, enabling system operators to identify critical fault features. The SHAP-based explainability framework plays a critical role in translating the model’s predictions into actionable insights, ensuring that the proposed solution is not only accurate but also practically implementable in real-world scenarios. The results demonstrate the QDNN framework’s superior accuracy in fault localization even in noisy environments and with high-resistance faults, independent of voltage levels and DCMG configurations, making it a robust solution for modern power systems.

Suggested Citation

  • Amir Hossein Poursaeed & Farhad Namdari, 2025. "Explainable AI-Driven Quantum Deep Neural Network for Fault Location in DC Microgrids," Energies, MDPI, vol. 18(4), pages 1-29, February.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:4:p:908-:d:1590478
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    References listed on IDEAS

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    1. Hamed Binqadhi & Waleed M. Hamanah & Md Shafiullah & Md Shafiul Alam & Mohammad M. AlMuhaini & Mohammad A. Abido, 2024. "A Comprehensive Survey on Advancement and Challenges of DC Microgrid Protection," Sustainability, MDPI, vol. 16(14), pages 1-22, July.
    2. 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).
    3. Sadaqat Ali & Zhixue Zheng & Michel Aillerie & Jean-Paul Sawicki & Marie-Cécile Péra & Daniel Hissel, 2021. "A Review of DC Microgrid Energy Management Systems Dedicated to Residential Applications," Energies, MDPI, vol. 14(14), pages 1-26, July.
    4. Bayati, Navid & Balouji, Ebrahim & Baghaee, Hamid Reza & Hajizadeh, Amin & Soltani, Mohsen & Lin, Zhengyu & Savaghebi, Mehdi, 2022. "Locating high-impedance faults in DC microgrid clusters using support vector machines," Applied Energy, Elsevier, vol. 308(C).
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