scholarly journals Moisture content as an important integral parameter of transformer oil quality in power transformers of 10/0.4 kV substations

2019 ◽  
Vol 124 ◽  
pp. 05008
Author(s):  
V Trushkin ◽  
S Shlyupikov ◽  
G Eroshenko ◽  
M Levin ◽  
S Kifarak
Keyword(s):  
Moisture Content ◽  
Oil Quality ◽  
Transformer Oil ◽  
Power Transformers ◽  
Main Element ◽  
Integral Parameter ◽  
Information Analysis ◽  
Sample Test ◽  
Long Time ◽  
Transformer Insulation

The main element characterizing the transformer efficiency is the state of its isolation, and the first thing is operating oil, which can perform its functions for a long time with timely restoration of its properties. Transformer oil is the main dielectric being in contact with the external environment, interacting with solid insulation and carrying a large amount of diagnostic information. Analysis of the development of transformer insulation damage showed that the main catalyst for accelerating the aging process of the oil is the appearance of moisture in it. This effect is enhanced by oxygen, temperature, the catalytic action of metals, oxidation and other factors. To confirm the influence of moisture content in oil on the deterioration of its properties, a statistical analysis of the oil sample test protocols was carried out. The obtained data allowed us to determine the moisture content as an important integral parameter of oil quality. Recommendations are given on the inclusion of a moisture content parameter in a set of mandatory requirements, in particular, for 10/0.4 kV consumer transformer substations equipped with silica gel air dryers

scholarly journals Acoustic Control Method of Quality Characteristics of New Transformer Oil

2019 ◽  
pp. 746-752
Author(s):  
Anastasiia V. Krekhova ◽  
Yuriy N. Bezborodov ◽  
Andrey P. Batrak
Keyword(s):  
Control Method ◽  
Oil Quality ◽  
Operation Time ◽  
Acoustic Method ◽  
Operational Reliability ◽  
Quality Characteristics ◽  
Transformer Oil ◽  
Power Transformers ◽  
Acoustic Control

Power transformers are the most expensive and strategically important components of any power generation and transmission system. Diagnostics methods result from the necessity to provide the operational reliability of power equipment and extend its operation time. Acoustic method is one of the perspective methods to determine transformer oil quality. In comparison with standard methods the acoustic method have follow important advantages: low labor intensity, need lack of the difficult expensive equipment, carrying out researches in vitro frames and high qualification of researches. This study represents results of impurity influence investigation (water and cellulose) on an acoustic range of new transformer oil

scholarly journals Dissolved Gas Analysis Equipment for Online Monitoring of Transformer Oil: A Review

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4057 ◽  
Author(s):  
Sergio Bustamante ◽  
Mario Manana ◽  
Alberto Arroyo ◽  
Pablo Castro ◽  
Alberto Laso ◽  
...  
Keyword(s):  
Electric Power ◽  
Online Monitoring ◽  
Oil Quality ◽  
Gas Analysis ◽  
Transformer Oil ◽  
Power Transformers ◽  
Quality Analysis ◽  
Dissolved Gas ◽  
Correct Operation ◽  
Dissolved Gas Analysis

Power transformers are the most important assets of electric power substations. The reliability in the operation of electric power transmission and distribution is due to the correct operation and maintenance of power transformers. The parameters that are most used to assess the health status of power transformers are dissolved gas analysis (DGA), oil quality analysis (OQA) and content of furfuraldehydes (FFA) in oil. The parameter that currently allows for simple online monitoring in an energized transformer is the DGA. Although most of the DGA continues to be done in the laboratory, the trend is online DGA monitoring, since it allows for detection or diagnosis of the faults throughout the life of the power transformers. This study presents a review of the main DGA monitors, single- or multi-gas, their most important specifications, accuracy, repeatability and measurement range, the types of installation, valve or closed loop, and number of analogue inputs and outputs. This review shows the differences between the main existing DGA monitors and aims to help in the selection of the most suitable DGA monitoring approach according to the needs of each case.

scholarly journals Study on Sulfide Distribution in the Operating Oil of Power Transformers and Its Effect on the Oil Quality

2018 ◽  
Vol 8 (9) ◽  
pp. 1577 ◽  
Author(s):  
Haoxi Cong ◽  
Minhao Zhang ◽  
Qingmin Li
Keyword(s):  
Quantitative Measurement ◽  
Copper Wire ◽  
Oil Quality ◽  
Transformer Oil ◽  
Power Transformers ◽  
Engineering Practice ◽  
Insulation Failure ◽  
Alkyl Groups ◽  
Cuprous Sulfide ◽  
Transformer Oils

Corrosive sulfides in transformer oil could react with copper wire to produce cuprous sulfide, causing insulation failure. At present, both the quantitative measurement method and distribution of sulfur components in operating oil are not clear yet. In this paper, the existing types and contents of sulfides in oil samples with different alkyl groups and different voltage levels were investigated. With quantitative testing methods, the distribution of sulfur composition in the operating oil was analyzed. Results showed that the thiophene sulfide in transformer oil existed mainly in the form of benzothiophene with an unsaturation of 6 and dibenzothiophene with an unsaturation of 9. The content of monosulfide sulfide with unsaturation of 3 or 6 was the highest. The disulfide existed basically in the form of Dibenzyl disulfide (DBDS). The influence of sulfides on the oil quality were analyzed on this basis. Results showed that the existence of sulfides would increase the moisture content in oil. The absorbed moisture could cause the decrease of the breakdown voltage and rise of the dielectric loss. The above study could provide some engineering practice for understanding the sulphide distribution in transformer oils and further prevent the sulfur corrosion faults.

scholarly journals Reproducing of the humidity curve of power transformers oil using adaptive neuro-fuzzy systems

2021 ◽  
pp. 10-14
Author(s):  
V.V. Vasilevskij ◽  
M.O. Poliakov
Keyword(s):  
Neural Networks ◽  
Moisture Content ◽  
Continuous Monitoring ◽  
Fuzzy Neural Networks ◽  
Transformer Oil ◽  
Power Transformers ◽  
Subtractive Clustering ◽  
Grid Partition ◽  
Neuro Fuzzy ◽  
Fuzzy Neural

Introduction. One of the parameters that determine the state of the insulation of power transformers is the degree of moisture content of cellulose insulation and transformer oil. Modern systems of continuous monitoring of transformer equipment have the ability to accumulate data that can be used to reproduce the dynamics of moisture content in insulation. The purpose of the work is to reproduce the curve of the of humidity of transformer oil based on the results of measuring the temperature of the upper and lower layers of oil without the need for direct measurement of moisture content by special devices. Methodology. The construction of a fuzzy neural network is carried out using networks based on adaptive neuro-fuzzy system ANFIS. The network generated using the Grid Partition algorithm without clustering and Subtractive Clustering. Results. The paper presents a comparative analysis of fuzzy neural networks of various architectures in terms of increasing the accuracy of reproducing the moisture content of transformer oil. For training and testing fuzzy neural networks, the results of continuous monitoring of the temperature of the upper and lower layers of transformer oil during two months of operation used. Considered twenty four variants of the architecture of ANFIS models, which differ in the membership functions, the number of terms of each input quantity, and the number of training cycles. The results of using the constructed fuzzy neural networks for reproducing the dynamics of moisture content of transformer oil during a month of operation of the transformer are presented. The reproducing accuracy was assessed using the root mean square error and the coefficient of determination. The test results indicate the sufficient adequacy of the proposed models. Consequently, the RMSE value for the network constructed using Grid Partition method was 0.49, and for the network built using the Subtractive Clustering method – 0.40509.

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.

scholarly journals Predicting the influence of the non-sinusoidal network mode on power transformers

2019 ◽  
Vol 114 ◽  
pp. 04005
Author(s):  
Ngo Van Cuong ◽  
Lidiia I. Kovernikova
Keyword(s):  
Service Life ◽  
Electric Power ◽  
Power Quality ◽  
Electrical Equipment ◽  
Operating Conditions ◽  
Transformer Oil ◽  
Power Transformers ◽  
Electrical Network ◽  
Electrical Networks ◽  
Current Harmonics

The parameters of electrical network modes often do not meet the requirements of Russian GOST 32144-2013 and the guidelines of Vietnam. In the actual operating conditions while there is the non-sinusoidal mode in electrical networks voltage and current harmonics are present. Harmonics result in overheating and damage of power transformers since they cause additional active power losses. Additional losses lead to the additional heat release, accelerating the process of insulating paper, transformer oil and magnetic structure deterioration consequently shortening the service life of a power transformer. In this regard there arises a need to develop certain scientific methods that would help demonstrate that low power quality, for instance could lead to a decrease in the electrical equipment service life. Currently we see a development of automated systems for continuous monitoring of power quality indices and mode parameters of electrical networks. These systems could be supplemented by characteristics calculating programs that give out a warning upon detection of the adverse influence of voltage and current harmonics on various electrical equipment of both electric power providers and electric power consumers. A software program presented in the article may be used to predict the influence of voltage and current harmonics on power transformers.

Thermal Behavior of Power Transformers Filled With Waste Vegetable Oil-Based Biodiesel Under Dynamic Load

2021 ◽  
Vol 143 (9) ◽  
Author(s):  
Ehsan Ebrahimnia-Bajestan ◽  
Hani Tiznobaik ◽  
Paul Gheorghe ◽  
Mohammad Arjmand
Keyword(s):  
Heat Flux ◽  
Ambient Temperature ◽  
Vegetable Oil ◽  
Dynamic Load ◽  
Renewable Resource ◽  
High Heat ◽  
Transformer Oil ◽  
Power Transformers ◽  
High Heat Flux ◽  
Aging Rate

Abstract Petroleum-based oils are widely used as electrically insulating materials in high voltage power transformers for dissipating high generated heat flux and maintaining the temperature below critical values. The operating temperature of a transformer dominantly governs its aging rate. In the present research, a renewable coolant as a versatile substitution for the petroleum-based oils was investigated to be employed in the cooling of transformers. The studied coolant is a vegetable-based oil extracted from the waste cooking oils. A numerical model was developed to follow the instantaneous changes in the load profile and ambient temperature and predict the instantaneous hotspot temperature values in the transformer under dynamic load. Then, this thermal model was used to explore the capability of the studied vegetable oil in the cooling of transformers compared with conventional transformer oil. The realistic ambient temperature and loading profile, as well as thermal properties of oils and characteristics of a transformer, were applied as the model’s inputs. The aging rate of the transformer in the presence of vegetable oil was also compared with the conventional transformer oil. The results indicate a better cooling performance for the vegetable-based oil, where a hotspot temperature reduction of 3 °C was observed in comparison to the petroleum-based oil. Also, the model predicts a significantly longer life for the insulating system of the transformer when the proposed vegetable-based oil is employed. The results of this research suggest a sustainable way of reusing the waste of a renewable resource as an alternative insulating liquid for the cooling of high heat flux electric/electronic devices.

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