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Simultaneous Dissolved Gas Analysis in Transformer Oil via Time-Division-Multiplexed Quartz-Enhanced Photoacoustic Spectroscopy.

PubMed
Authors: Dai J, Zhang Y, Wang J, Wang C, Wang Y, Tian Q, Chen Y, Feng C, Cui R, Yin X, Dong L, Wu H

Year

2026

Paper ID

9854

Status

Peer-reviewed

Abstract Read

~2 min

Abstract Words

202

Citations

N/A

Abstract

Dissolved gas analysis (DGA) is an essential method for monitoring and diagnosing faults in oil-immersed transformers. Acetylene (CH) and methane (CH) are key indicator gases for fault identification. We report for the first time a time-division-multiplexed quartz-enhanced photoacoustic spectroscopy (QEPAS) sensing system capable of simultaneously and sensitively detecting dissolved CH and CH, even with an extremely small amount of required sample gas. The on-beam configuration enhanced the 2 signal amplitude by nearly 20-fold. The sensing system achieved minimum detection limits (MDLs) of approximately 15 ppb for CH and 0.3 ppm for CH, which are 2-3 orders of magnitude lower than the safety thresholds defined in the relevant industry standards. The detectability of the sensing system satisfies the requirements for DGA in transformer oil. With a gas cell volume of approximately 1.6 mL, the system markedly reduces the required oil sample volume. By integrating headspace degassing with QEPAS, the sensing system enables real-time monitoring and analysis of the oil-gas equilibrium behavior of dissolved CH and CH. With its high sensitivity, rapid response, and low sample consumption, the proposed sensing system provides a viable and efficient approach for early detection of transformer faults. Furthermore, it establishes a foundation for applying QEPAS to dissolved gas analysis in transformer oil.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2026 reference point for readers tracking recent quantum research.
  • Dissolved gas analysis (DGA) is an essential method for monitoring and diagnosing faults in oil-immersed transformers.

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