scholarly journals Photoluminescence Spectroscopy Measurements for Effective Condition Assessment of Transformer Insulating Oil

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 732
Author(s):  
Abdelrahman M. Alshehawy ◽  
Diaa-Eldin A. Mansour ◽  
Mohsen Ghali ◽  
Matti Lehtonen ◽  
Mohamed M. F. Darwish
Keyword(s):  
Electronic States ◽  
Dissipation Factor ◽  
Condition Assessment ◽  
Transformer Oil ◽  
Term Operation ◽  
Pl Spectra ◽  
Insulating Oil ◽  
Vis Spectroscopy ◽  
Effective Condition ◽  
Dielectric Dissipation Factor

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 which typically yield a broad spectrum, and thus cannot precisely reflect the electronic structure of aged oil samples. It also cannot be implemented as an online sensor of oil degradation. In this paper, photoluminescence (PL) spectroscopy is introduced, for the first time, for effective condition assessment of insulating oil. The PL technique involves emission processes that only occur between a narrow band of electronic states that are occupied by thermalized electrons and consequently yields a spectrum that is much narrower than that of the absorption spectrum. Aged oil samples with different aging extents were prepared in the laboratory using accelerated aging tests at 120 °C, under which 1 day of laboratory aging is equivalent to approximately 1 year of aging in the field. These aged samples were then tested using PL spectroscopy with a wavelength ranging from 150 nm to 1500 nm. Two main parameters were evaluated for quantitative analysis of PL spectra: The full width at half-maximum and the enclosed area under the PL spectra. These parameters were correlated to the aging extent. In conjunction with PL spectroscopy, the aged oil samples were tested for the dielectric dissipation factor as an indication of the number of aging byproducts. Interestingly, we find a correlation between the PL spectra and the dielectric dissipation factor. The results of PL spectroscopy were compared to those of UV-Vis spectroscopy for the same samples and the parameters extracted from PL spectra were compared to the aging b-products extracted from UV-Vis spectra. Finally, the corresponding physical mechanisms were discussed considering the obtained results and the spectral shift for each spectrum. It was proved that PL spectroscopy is a promising technique for the condition assessment of insulating oil when compared to conventional transformer oil assessment measuring techniques and even to other optical absorption techniques.

scholarly journals The Correlation of Transformer Oil Electrical Properties with Water Content Using a Regression Approach

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2089
Author(s):  
Sifeddine Abdi ◽  
Noureddine Harid ◽  
Leila Safiddine ◽  
Ahmed Boubakeur ◽  
Abderrahmane (Manu) Haddad
Keyword(s):  
Regression Analysis ◽  
Electrical Properties ◽  
Water Content ◽  
Breakdown Voltage ◽  
Mobile Station ◽  
Dissipation Factor ◽  
Exponential Model ◽  
Transformer Oil ◽  
The Impact ◽  
Dielectric Dissipation Factor

An experimental investigation is conducted to measure and correlate the impact of the water content on the electrical characteristics of the mineral oil for transformers, particularly the breakdown voltage, the resistivity, and the dielectric dissipation factor. Regression method is carried out to compare the results obtained through laboratory experiments with those predicted using an analytical model. A treatment to reduce water content in oil involving filtration, degassing and dehydration using a SESCO mobile station was applied to the new, regenerated, and used oil samples in service. The breakdown voltage, the resistivity, and the dielectric dissipation factor of the samples were measured. Regression analysis using an exponential model was applied to examine the samples electrical properties. The results show that, after treatment, the breakdown voltage and resistivity increase as the water content decreases, unlike the dielectric dissipation factor which exhibits a decreasing trend. This trend is found to be similar for the three oil samples: new, regenerated, and used. The results of the regression analysis give close agreement with the experimental results for all the samples and all studied characteristics. The model shows strong correlation with high coefficients (>90%).

scholarly journals Transformer Oil Quality Assessment Using Random Forest with Feature Engineering

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1809
Author(s):  
Mohammed El Amine Senoussaoui ◽  
Mostefa Brahami ◽  
Issouf Fofana
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Oil Quality ◽  
Principal Component ◽  
Condition Assessment ◽  
Classification Performance ◽  
Transformer Oil ◽  
Classification Model ◽  
Insulation Degradation ◽  
Transformer Oils

Machine learning is widely used as a panacea in many engineering applications including the condition assessment of power transformers. Most statistics attribute the main cause of transformer failure to insulation degradation. Thus, a new, simple, and effective machine-learning approach was proposed to monitor the condition of transformer oils based on some aging indicators. The proposed approach was used to compare the performance of two machine-learning classifiers: J48 decision tree and random forest. The service-aged transformer oils were classified into four groups: the oils that can be maintained in service, the oils that should be reconditioned or filtered, the oils that should be reclaimed, and the oils that must be discarded. From the two algorithms, random forest exhibited a better performance and high accuracy with only a small amount of data. Good performance was achieved through not only the application of the proposed algorithm but also the approach of data preprocessing. Before feeding the classification model, the available data were transformed using the simple k-means method. Subsequently, the obtained data were filtered through correlation-based feature selection (CFsSubset). The resulting features were again retransformed by conducting the principal component analysis and were passed through the CFsSubset filter. The transformation and filtration of the data improved the classification performance of the adopted algorithms, especially random forest. Another advantage of the proposed method is the decrease in the number of the datasets required for the condition assessment of transformer oils, which is valuable for transformer condition monitoring.

Investigation of the Properties of Alumina-Zirconia Ceramics

2014 ◽  
Vol 1040 ◽  
pp. 245-249
Author(s):  
Aleksander S. Ivashutenko ◽  
Alexandr V. Kabyshev ◽  
Nikita Martyushev ◽  
Igor G. Vidayev
Keyword(s):  
Dielectric Constant ◽  
Electric Conductivity ◽  
High Temperatures ◽  
Electrostatic Field ◽  
Mechanical Characteristics ◽  
Ferroelectric Properties ◽  
Dissipation Factor ◽  
Zirconia Ceramics ◽  
Measurement Results ◽  
Dielectric Dissipation Factor

The article focuses on the investigation of the properties of alumina-zirconia ceramics possessing high mechanical characteristics and good conductivity at high temperatures. Measurement results of the dielectric dissipation factor, dielectric constant, electric conductivity when using direct and alternating current for the ceramics samples of 80%(ZrO2-3%Y2O3)-20% Al2O3 composition are presented in the paper. Measurements were conducted simultaneously in the electrostatic field in vacuum while heating the samples to the temperatures ranging from 300 to 1700K. Investigations showed that alumina-zirconia ceramics at high temperatures obtains ferroelectric properties not typical of these structures.

Sign in / Sign up

Export Citation Format

Share Document