Paper
6 August 2023 Fluorescence spectroscopy-based very sensitive detection technique for insulating oil conditions
Author Affiliations +
Proceedings Volume 12781, International Conference on Optoelectronic Information and Functional Materials (OIFM 2023); 127811O (2023) https://doi.org/10.1117/12.2686928
Event: 2023 International Conference on Optoelectronic Information and Functional Materials (OIFM 2023), 2023, Guangzhou, JS, China
Abstract
This research proposes a highly sensitive transformer fault detection method based on fluorescence spectral analysis to solve the problems of DGA (Dissolved Gases Analysis) technology, which is widely used for insulating oil condition detection: (1) the detection cycle is long and cannot respond in time; (2) the detection sensitivity is insufficient and fails when the amount of dissolved gas is small or there is no dissolved gas under low-energy fault. Fluorescence spectroscopy-based rapid detection technique. To master the fluorescence spectrum features of insulating oil and its ideal acquisition settings, the fluorescence spectral characteristics of commercially available new oil and insulating oil from real functioning transformers were gathered and evaluated. Perform a thermal aging fault simulation test, collect fluorescence spectra from defective oil samples to determine the ideal excitation wavelength, and link the fluorescence bimodal characteristic ratio with the duration of the thermal aging fault. The comparison results show that the fluorescence spectroscopy analysis method can detect the fault on the 30th day of thermal aging, which is approximately ten days earlier than the DGA method, shows how the test can assist fluorescence analysis approaches in detecting failures at an earlier stage. Meanwhile, fluorescence spectroscopic detection and analysis are quick, allowing for online real-time defect monitoring. The approach has the benefits of being quick and sensitive and not requiring sample treatment, and it has a promising future in the field of transformer failure identification.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lu Mu, DaCheng Li, AnJing Wang, Jia Xie, Yue Zhao, and XiaoBing Sun "Fluorescence spectroscopy-based very sensitive detection technique for insulating oil conditions", Proc. SPIE 12781, International Conference on Optoelectronic Information and Functional Materials (OIFM 2023), 127811O (6 August 2023); https://doi.org/10.1117/12.2686928
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KEYWORDS
Fluorescence

Emission wavelengths

Fluorescence intensity

Transformers

Fluorescence spectroscopy

Statistical analysis

Spectroscopy

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