|
|
|
|
|
|
Research on Electric Breakdown Fault Diagnosis Model of Transformer Insulated Oil Based on Fluorescent Double-Color Ratio |
ZHAO Yue2, MA Feng-xiang2, WANG An-jing1*, LI Da-cheng1, SONG Yu-mei2, WU Jun1, CUI Fang-xiao1, LI Yang-yu1, CAO Zhi-cheng1 |
1. Anhui Institute of Optics and Fine Mechanics, Key Laboratory of General Optical Calibration and Characterization Technology, Chinese Academy of Sciences, Hefei 230031, China
2. State Grid Anhui Electric Power Research Institute, Hefei 230000, China
|
|
|
Abstract Traditional fluorescence analysis and detection methods of transformer insulated oil quality, which use fluorescence spectrophotometer to collect the full bands’ fluorescence spectrum of the oil sample and establish the diagnostic model of transformer operation states using the full bands’ fluorescence characteristics of insulating oil with a different aging degree, have the problems of large volume and the high price of photometer and the inability to realize real-time monitoring due to a long time of spectrum acquisition, a new method to detect the quality of transformer insulating oil based on the fluorescent double-color ratio to extract fluorescence characteristic dual band information and establish fault diagnosis model of transformer operation was raised to solve it. Thus, the traditional fluorescence spectrophotometer can be replaced by custom filters and visible photodetectors to realize the rapid acquisition and processing of double-color fluorescence information, which can meet the on-line monitoring and reduce the hardware cost. The aging of transformer insulating oil caused by discharge breakdown fault was analyzed by fluorescence analysis. Different discharge breakdown conditions were simulated. NYNAS oil samples with different discharge breakdown times (10, 30, 50, 70, 90 and 120 min) were prepared as fluorescence detection targets. Fluorescence emission spectra at different excitation wavelengths were collected by fluorescence spectrophotometer, and the optimal fixed excitation wavelength was found to be 280 nm. The 3-point moving mean smoothing method was used to smooth the fluorescence spectrum of the samples and by analyzing the variation of the fluorescence characteristic peak of the oil sample under different discharge breakdown times, bands of 380~388 and 399~407 nm were selected as the double-color information extraction band. The fault diagnosis model of transformer insulated oil discharge based on fluorescent double-color ratio was established by least-square curve fitting. The study results demonstrate the effectiveness of the fluorescent double-color method on the fault diagnosis of transformer insulated electric oil breakdown, which provides a theoretical and practical basis for establishing a small, low-cost, fast and effective online monitoring system.
|
Received: 2021-03-22
Accepted: 2021-05-28
|
|
Corresponding Authors:
WANG An-jing
E-mail: ajwang@aiofm.ac.cn
|
|
[1] Backhus D A,Gschwend P M. Environmental Science & Technology,1990,24:1214.
[2] Yeo T L,Ozanyan K B,Hindle F,et al. Appl. Spectrosc.,2002,56:846.
[3] Hindle F P,Yeo T L,Ozanyan K B,et al. IEEE Sensors Journal, 2003,3:766.
[4] Ozanyan K B,Yeo T L,Hindle F P,et al. IEEE Sensors Journal, 2004,4:681.
[5] Markova L,Myshkin N,Ossia C,et al. Tribology in Industry,2007,29:33.
[6] Deepa S,Sarathi R,Mishra K. Talanta,2006,70:811.
[7] Godinho M S,Blanco M R,Neto F G,et al. Talanta,2014,129:143.
[8] Fofana I,Bouaicha, A,Farzaneh M,et al. Electric Power Applications,2010,4:177.
[9] Abdi S,Boubakeur A,Haddad, A,et al. Electric Power Components and Systems,2011,39:1701.
[10] SI Shang-hua,ZHAO Jing-zhou,ZOU Guo-liang,et al(斯尚华,赵靖舟,邹国亮,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2020,40(6):1736.
|
[1] |
LEI Hong-jun1, YANG Guang1, PAN Hong-wei1*, WANG Yi-fei1, YI Jun2, WANG Ke-ke2, WANG Guo-hao2, TONG Wen-bin1, SHI Li-li1. Influence of Hydrochemical Ions on Three-Dimensional Fluorescence
Spectrum of Dissolved Organic Matter in the Water Environment
and the Proposed Classification Pretreatment Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 134-140. |
[2] |
GU Yi-lu1, 2,PEI Jing-cheng1, 2*,ZHANG Yu-hui1, 2,YIN Xi-yan1, 2,YU Min-da1, 2, LAI Xiao-jing1, 2. Gemological and Spectral Characterization of Yellowish Green Apatite From Mexico[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 181-187. |
[3] |
HAN Xue1, 2, LIU Hai1, 2, LIU Jia-wei3, WU Ming-kai1, 2*. Rapid Identification of Inorganic Elements in Understory Soils in
Different Regions of Guizhou Province by X-Ray
Fluorescence Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 225-229. |
[4] |
WANG Hong-jian1, YU Hai-ye1, GAO Shan-yun1, LI Jin-quan1, LIU Guo-hong1, YU Yue1, LI Xiao-kai1, ZHANG Lei1, ZHANG Xin1, LU Ri-feng2, SUI Yuan-yuan1*. A Model for Predicting Early Spot Disease of Maize Based on Fluorescence Spectral Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3710-3718. |
[5] |
CHENG Hui-zhu1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, MA Qian1, 2, ZHAO Yan-chun1, 2. Genetic Algorithm Optimized BP Neural Network for Quantitative
Analysis of Soil Heavy Metals in XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3742-3746. |
[6] |
SONG Yi-ming1, 2, SHEN Jian1, 2, LIU Chuan-yang1, 2, XIONG Qiu-ran1, 2, CHENG Cheng1, 2, CHAI Yi-di2, WANG Shi-feng2,WU Jing1, 2*. Fluorescence Quantum Yield and Fluorescence Lifetime of Indole, 3-Methylindole and L-Tryptophan[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3758-3762. |
[7] |
YANG Ke-li1, 2, PENG Jiao-yu1, 2, DONG Ya-ping1, 2*, LIU Xin1, 2, LI Wu1, 3, LIU Hai-ning1, 3. Spectroscopic Characterization of Dissolved Organic Matter Isolated From Solar Pond[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3775-3780. |
[8] |
LI Xiao-li1, WANG Yi-min2*, DENG Sai-wen2, WANG Yi-ya2, LI Song2, BAI Jin-feng1. Application of X-Ray Fluorescence Spectrometry in Geological and
Mineral Analysis for 60 Years[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 2989-2998. |
[9] |
XUE Fang-jia, YU Jie*, YIN Hang, XIA Qi-yu, SHI Jie-gen, HOU Di-bo, HUANG Ping-jie, ZHANG Guang-xin. A Time Series Double Threshold Method for Pollution Events Detection in Drinking Water Using Three-Dimensional Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3081-3088. |
[10] |
MA Qian1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, CHENG Hui-zhu1, 2, ZHAO Yan-chun1, 2. Research on Classification of Heavy Metal Pb in Honeysuckle Based on XRF and Transfer Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2729-2733. |
[11] |
JIA Yu-ge1, YANG Ming-xing1, 2*, YOU Bo-ya1, YU Ke-ye1. Gemological and Spectroscopic Identification Characteristics of Frozen Jelly-Filled Turquoise and Its Raw Material[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2974-2982. |
[12] |
YANG Xin1, 2, XIA Min1, 2, YE Yin1, 2*, WANG Jing1, 2. Spatiotemporal Distribution Characteristics of Dissolved Organic Matter Spectrum in the Agricultural Watershed of Dianbu River[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2983-2988. |
[13] |
CHEN Wen-jing, XU Nuo, JIAO Zhao-hang, YOU Jia-hua, WANG He, QI Dong-li, FENG Yu*. Study on the Diagnosis of Breast Cancer by Fluorescence Spectrometry Based on Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2407-2412. |
[14] |
ZHU Yan-ping1, CUI Chuan-jin1*, CHENG Peng-fei1, 2, PAN Jin-yan1, SU Hao1, 2, ZHANG Yi1. Measurement of Oil Pollutants by Three-Dimensional Fluorescence
Spectroscopy Combined With BP Neural Network and SWATLD[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2467-2475. |
[15] |
LIU Xian-yu1, YANG Jiu-chang1, 2, TU Cai1, XU Ya-fen1, XU Chang3, CHEN Quan-li2*. Study on Spectral Characteristics of Scheelite From Xuebaoding, Pingwu County, Sichuan Province, China[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2550-2556. |
|
|
|
|