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Data-driven digital twin for fault detection in compressed air energy storage systems: Design and experimental validation

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  • Semeraro, Concetta
  • Ababneh, Rawnaq Faisal
  • Alkhatib, Lamis Ahmed
  • Saqallah, Dana
  • Al Koutoubi, Rawad
  • Aljaghoub, Haya
  • Alami, Abdul Hai
  • Abdelkareem, Mohammad Ali
  • Olabi, Abdul Ghani

Abstract

Renewable energy resources have emerged as a sustainable alternative to fossil fuels; however, their reliability is often compromised by their dependence on fluctuating and uncontrollable environmental conditions. To mitigate this variability, Compressed Air Energy Storage (CAES) systems are an effective solution for storing renewable energy. Additionally, despite their advantages, CAES systems may lead to potential system failures, which limit their operational effectiveness. To overcome these problems, this study presents the design and development of a digital twin methodology tailored for the CAES system. The proposed digital twin methodology integrates real-time data acquisition, data-driven modeling techniques, and patterns library formalization to improve Digital Twin design and identify potential failures.

Suggested Citation

  • Semeraro, Concetta & Ababneh, Rawnaq Faisal & Alkhatib, Lamis Ahmed & Saqallah, Dana & Al Koutoubi, Rawad & Aljaghoub, Haya & Alami, Abdul Hai & Abdelkareem, Mohammad Ali & Olabi, Abdul Ghani, 2025. "Data-driven digital twin for fault detection in compressed air energy storage systems: Design and experimental validation," Energy, Elsevier, vol. 336(C).
    • Handle: RePEc:eee:energy:v:336:y:2025:i:c:s0360544225040435
    DOI: 10.1016/j.energy.2025.138401
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