Fault diagnosis methods of transformer based on dissolved gas detection in oil by gas chromatography

Zi'en Liu; Yue Zhao; Zhenghao Li; Jun Cao; Guoping Sheng

doi:10.1117/12.3024626

13 May 2024 Fault diagnosis methods of transformer based on dissolved gas detection in oil by gas chromatography

Zi'en Liu, Yue Zhao, Zhenghao Li, Jun Cao, Guoping Sheng

Author Affiliations +

Proceedings Volume 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023); 131595A (2024) https://doi.org/10.1117/12.3024626
Event: Eighth International Conference on Energy System, Electricity and Power (ESEP 2023), 2023, Wuhan, China

Abstract

Large transformer is the core equipment in power system, the diagnosis and prevention of equipment fault is very important to the safe operation of power system. At high temperature and high voltage, dissolved gas produced by the decomposition of transformer oil is an important indicator of transformer operation status. However, due to the low content of dissolved gas and the complexity of the measurement processes, it is easy to produce errors, which brings great challenges to the accurate detection of dissolved gas. In addition, how to establish the correct relationship between the content of dissolved gas components with the types and degrees of transformer fault also needs to be studied. Therefore, this paper first clarified the measurement processes of dissolved gas in transformer oil, then analysed the possible error sources of each link, then introduced common error assessment methods and proposed feasible methods to reduce dissolved gas test errors, and finally introduced the application of artificial intelligence to fault diagnosis of transformers based on dissolved gas content. This paper will provide some feasible theoretical support for reducing the measurement error of dissolved gas in transformer oil and accurately diagnosing transformer faults.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zi'en Liu, Yue Zhao, Zhenghao Li, Jun Cao, and Guoping Sheng "Fault diagnosis methods of transformer based on dissolved gas detection in oil by gas chromatography", Proc. SPIE 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023), 131595A (13 May 2024); https://doi.org/10.1117/12.3024626

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KEYWORDS

Transformers

Error analysis

Gases

Measurement uncertainty

Wavelets

Chromatography

Equipment

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