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

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

Energy, 2025, vol. 336, issue C

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.

Keywords: Digital twin; Pattern detection; Data-driven approach; Compressed air energy storage system; Cyber-physical system; Energy storage system (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:336:y:2025:i:c:s0360544225040435

DOI: 10.1016/j.energy.2025.138401

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