Abstract:Accurate assessment of transformer oil-paper insulating thermal aging serves as an important part to ensure the safe operation of power equipment. The successful application of Near Infrared Spectroscopy in petrochemicals and other fields provides new ideas for electrical insulation testing. The accelerated thermal aging test has experimented in a vacuum environment of 130 ℃. Fourteen groups of samples with different aging time are prepared. The spectrum of the aged insulating oil was collected by the Near Infrared Spectroscopy, and the furfural content in transformer oil was detected by high performance liquid chromatography(HPLC). There are obvious absorption peaks at 8 373, 8 264, 7 181, 7 076, 6 981, 5 855, 5 799, and 5 678 cm-1 in the original spectrum. This study specifically analyzes the attribution of each absorption peak. The original spectrum was preprocessed using a five-point cubic polynomial Savitzky-Golay convolution smoothing algorithm. The characteristic spectral regions for aging time are selected as 11 209~10 364, 9 087~7 818, 7 390~4 424 cm-1, with a total of 1 320 wavelength points. At the same time, the spectral information of the characteristic region is extracted by PCA, which indicates that the cumulative contribution rate of the first seven principal components is 99.78%. On the basis of the above, a PCR, PLSR, PCA-BP-ANN prediction model for aging time was established. It is shown that the PCA-BP-ANN aging time prediction model with conjugate gradient algorithm is the best, with RMSEP of 18.67 and R2 of 0.997 3. The characteristic spectral region of the furfural content in the oil is selected from 9 107 to 4 424 cm-1 for a total of 1210 wavelength points. At the same time, the spectral information of the characteristic region is extracted by PCA, which indicates that the cumulative contribution rate of the first four principal components is 99.96%. On the basis of the above, a PCR, PLSR, PCA-BP-ANN prediction model for the content of furfural in oil was established. It is shown that the PCA-BP-ANN furfural content prediction model with conjugate gradient algorithm performs best, with RMSEP of 0.134 4 and R2 of 0.987 7. It is feasible to evaluate the thermal aging time and the furfural content based on near-infrared spectroscopy of insulating oil.
蒋友列,祝诗平,唐 超,孙碧云,王 亮. 绝缘油热老化时间及糠醛含量的近红外光谱快速预测方法[J]. 光谱学与光谱分析, 2020, 40(11): 3515-3521.
JIANG You-lie, ZHU Shi-ping, TANG Chao, SUN Bi-yun, WANG Liang. Fast Prediction Method of Thermal Aging Time and Furfural Content of Insulating Oil Based on Near-Infrared Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(11): 3515-3521.
[1] LIAO Rui-jin, YANG Li-jun, ZHENG Han-bo, et al(廖瑞金, 杨丽君, 郑含博, 等). Transactions of China Electrotechnical Society(电工技术学报), 2012, 27(5): 1.
[2] YANG Li-jun, PENG Pan, GAO Jun, et al(杨丽君, 彭 攀, 高 竣, 等). Transactions of China Electrotechnical Society(电工技术学报), 2018, 33(9): 179.
[3] FAN Zhou, CHEN Wei-gen, WAN Fu, et al(范 舟, 陈伟根, 万 福, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2018, 38(10): 3117.
[4] GU Zhao-liang, CHEN Wei-gen, DU Ling-ling, et al(顾朝亮, 陈伟根, 杜玲玲, 等). Proceedings of the CSEE(中国电机工程学报), 2017, 37(19):5804.
[5] CHU Xiao-li(褚小立). Molecular Spectroscopy Analytical Technology Combined with Chemometrics and Its Applications(化学计量学方法与分子光谱分析技术). Beijing: Chemical Industry Press(北京: 化学工业出版社), 2011.
[6] LI Jing-yan, CHU Xiao-li, CHEN Pu, et al(李敬岩, 褚小立, 陈 瀑, 等). Acta Petrolei Sinica(石油学报), 2017, 33(1): 131.
[7] Wang Lu, Sun Dawen, Pu Hongbin, et al. Critical Reviews in Food Science and Nutrition, 2017, 57(7): 1524.
[8] Cascant M M, Breil C, Fabiano-Tixier A S, et al. Food Chemistry, 2018, 239: 865.
[9] IEEE Std C57.91. IEEE Guide for Loading Mineral-Oil Immersed Transformers,2011.
[10] National Energy Administration of China(国家能源局). Method for Determination of Furfural Content in Transformer Oil by HPLC(DL/T 1355—2014) (变压器油中糠醛含量的测定—液相色谱法). Beijing: China Electric Power Press(北京: 中国电力出版社),2014.
[11] WANG Wei, DONG Wen-yan, JIANG Da, et al(王 伟, 董文妍, 蒋 达, 等). Insulating Materials(绝缘材料), 2018, 51(5): 7.
[12] Norgaard L, Saudland A, Wagner J, et al. Applied Spectroscopy,2000, 54(3): 413.