Detection of Dissolved Carbon Monoxide in Transformer Oil Using 1.567 m Diode Laser-Based Photoacoustic SpectroscopyReportar como inadecuado

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Journal of Spectroscopy - Volume 2015 2015, Article ID 737635, 7 pages -

Research Article

College of Engineering and Technology, Southwest University, Chongqing 400715, China

State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400030, China

Received 6 August 2014; Revised 10 October 2014; Accepted 11 October 2014

Academic Editor: Wen Zeng

Copyright © 2015 Qu Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Carbon monoxide CO is one of the most important fault characteristic gases dissolved in power transformer oil. With the advantages of high sensitivity and accuracy, long-term stability, and short detection time, photoacoustic spectroscopy PAS has been proven to be one promising sensing technology for trace gas recognition. In this investigation, a tunable PAS experimental system based on a distributed-feedback DFB diode laser was proposed for recognizing dissolved CO in transformer oil. The molecular spectral line of CO gas detection was selected at 1.567 μm in the whole experiment. Relationships between the photoacoustic PA signal and gas pressure, temperature, laser power, and CO gas concentration were measured and discussed in detail, respectively. Finally, based on the least square regression theory, a novel quantitative identification method for CO gas detection with the PAS experimental system was proposed. And a comparative research about the gas detection performances performed by the PAS system and gas chromatography GC measurement was presented. All results lay a solid foundation for exploring a portable and tunable CO gas PAS detection device for practical application in future.

Autor: Qu Zhou, Chao Tang, Shiping Zhu, Weigen Chen, and Xiaojuan Peng



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