2021
DOI: 10.3390/pr9050732
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Photoluminescence Spectroscopy Measurements for Effective Condition Assessment of Transformer Insulating Oil

Abstract: Condition assessment of insulating oil is crucial for the reliable long-term operation of power equipment, especially power transformers. Under thermal aging, critical degradation in oil properties, including chemical, physical, and dielectric properties, occurs due to the generation of aging byproducts. Ultraviolet-visible (UV-Vis) spectroscopy was recently proposed for the condition assessment of mineral oil. However, this absorption technique may involve all electronic states of the investigated material wh… Show more

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Cited by 67 publications
(39 citation statements)
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“…In the case of free-standing particles, regulation of the dynamics (i.e., pressure, temperature, number of cycles) of the extrusion process, as well as a study focusing on the effect of the secondary size of the dispersoids on transmission and reflection spectra, are expected to lead to further optimization of their optical performance. Furthermore, complementary characterization techniques, such as spectroscopic ellipsometry [46,47] for the extraction of complex refractive index values, and photoluminescence [48] for the determination of sample aging, can lead to a more comprehensive mapping of their properties.…”
Section: Resultsmentioning
confidence: 99%
“…In the case of free-standing particles, regulation of the dynamics (i.e., pressure, temperature, number of cycles) of the extrusion process, as well as a study focusing on the effect of the secondary size of the dispersoids on transmission and reflection spectra, are expected to lead to further optimization of their optical performance. Furthermore, complementary characterization techniques, such as spectroscopic ellipsometry [46,47] for the extraction of complex refractive index values, and photoluminescence [48] for the determination of sample aging, can lead to a more comprehensive mapping of their properties.…”
Section: Resultsmentioning
confidence: 99%
“…Owing to its outstanding ability to extract characteristics from various complex data, machine learning has been widely used in transformer state research. Many scholars employed online monitoring data including oil temperature, dissolved gases in oil, partial discharge, photoluminescence spectroscopy parameters etc., to perform parameters prediction [6][7][8], assess or forecast insulation ageing [9][10][11], and fault identification [12][13][14]. Benmahamed et al [15] proposed the method based on the support vector machine (SVM) and the K-nearest neighbour (KNN) to classify transformer insulating oil fault using dissolved gases.…”
Section: Introductionmentioning
confidence: 99%
“…Consequently, DC microgrids have been proven as one of the most efficient and cost-effective systems in the integration of RES with loads, as they decrease the AC-DC and DC-AC power conversion stages compared to AC microgrids [10,11]. Machine learning and artificial intelligence have shown promising performance in different electrical engineering applications [12][13][14][15][16] as well as power system components, e.g., power transformers and high voltage transmission lines [17][18][19][20][21][22][23]. Figure 1 illustrates the microgrid components in which the load and the diesel generator along with the wind turbines are connected to the AC side.…”
Section: Introductionmentioning
confidence: 99%