Investigation of Transformer Oil Properties with advanced Multidimensional Methods

2021 ◽  
Author(s):  
Sebastian Schreiter ◽  
Holger Lohmeyer ◽  
Peter Werle
Keyword(s):  
Transformer Oil ◽  
Multidimensional Methods

Facet Theory

1998 ◽  
Vol 3 (1) ◽  
pp. 13-36 ◽  
Author(s):  
Ruth Guttman ◽  
Charles W. Greenbaum
Keyword(s):  
Data Analysis ◽  
Animal Behavior ◽  
Infant Development ◽  
Facet Theory ◽  
Structural Relationships ◽  
Future Challenges ◽  
Spatial Configurations ◽  
Multidimensional Methods ◽  
Definition Of ◽  
Scalogram Analysis

This article gives an overview of Facet Theory, a systematic approach to facilitating theory construction, research design, and data analysis for complex studies, that is particularly appropriate to the behavioral and social sciences. Facet Theory is based on (1) a definitional framework for a universe of observations in the area of study; (2) empirical structures of observations within this framework; (3) a search for correspondence between the definitional system and aspects of the empirical structure for the observations. The development of Facet Theory and Facet Design is reviewed from early scale analysis and the Guttman Scale, leading to the concepts of “mapping sentence,” “universe of content,” “common range,” “content facets,” and nonmetric multidimensional methods of data analysis. In Facet Theory, the definition of the behavioral domain provides a rationale for hypothesizing structural relationships among variables employed in a study. Examples are presented from various areas of research (intelligence, infant development, animal behavior, etc.) to illustrate the methods and results of structural analysis with Smallest Space Analysis (SSA), Multidimensional Scalogram Analysis (MSA), and Partial Order Scalogram Analysis (POSA). The “radex” and “cylindrex” of intelligence tests are shown to be outstanding examples of predicted spatial configurations that have demonstrated the ubiquitous emergence of the same empirical structures in different studies. Further examples are given from studies of spatial abilities, infant development, animal behavior, and others. The use of Facet Theory, with careful construction of theory and design, is shown to provide new insights into existing data; it allows for the diagnosis and discrimination of behavioral traits and makes the generalizability and replication of findings possible, which in turn makes possible the discovery of lawfulness. Achievements, issues, and future challenges of Facet Theory are discussed.

Use of Waste Transformer Oil as a Basis for the Preparation of the Lubricant Composition for Drilling Mud

2019 ◽  
pp. 59-64
Author(s):  
A.V. Nekhorosheva ◽  
◽  
V.P. Nekhoroshev ◽  
I.V. Lodina ◽  
◽  
...  
Keyword(s):  
Transformer Oil ◽  
Drilling Mud ◽  
Lubricant Composition

On the effect of the periodicity of diagnostic measurements on improving the reliability of high-voltage transformers

2019 ◽  
Vol 12 (1) ◽  
pp. 18-21
Author(s):  
V. A. Tikhonov
Keyword(s):  
Service Life ◽  
High Voltage ◽  
Chromatographic Analysis ◽  
Aluminum Industry ◽  
Early Stage ◽  
Diagnostic Methods ◽  
Transformer Oil ◽  
Operating Voltage ◽  
Dissolved Gases ◽  
The Aluminum Industry

The influence of the periodicity of diagnostic measurements on the operational state of high-voltage transformers is considered. Examples of defects of switching devices of converter transformers and methods for their detection are given. The rationale for the importance of recognition of defects at an early stage of their occurrence is given. The influence of the multiplicity of overvoltages on the service life of converter transformers in the aluminum industry is investigated. Based on the analysis of the service life of converter transformers of one of the powerful aluminum plants, where 83% of converter transformers have exhausted their standard service life, it is shown that in 40% of cases it would be possible to avoid their failures, with timely detection and elimination of emerging defects. Examples of defects of OLR (on-load regulators) of converter transformers and methods for their detection are given. The importance of recognition of defects at an early stage of their occurrence is substantiated. A method for chromatographic analysis of dissolved gases in transformer oil has been developed for the qualitative determination of defects and ways to eliminate them. Examples of diagnostics of converter transformers at operating voltage and working load are given, providing the best quality operational characteristics of converter transformers. The periodicity of diagnostic measurements and the reduction of defects and failures has been substantiated. The question of diagnosing the state of the converter transformer TDNP-40000/10 at an enterprise of the aluminum industry is investigated. Currently, diagnostic methods are being developed based on chromatographic analysis of dissolved gases in transformer oil. The presented method of evaluating the operating parameters of transformers allows for the safe operation of high-voltage transformers and enables to increase the reliability of the power supply scheme of aluminum industry plants.

Effect of Powder-Mixed Dielectric on EDM Process Performance

2020 ◽  
Vol 38 (8A) ◽  
pp. 1226-1235
Author(s):  
Safa R. Fadhil ◽  
Shukry. H. Aghdeab
Keyword(s):  
Silicon Carbide ◽  
High Speed ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
High Speed Steel ◽  
Transformer Oil ◽  
Dielectric Fluid ◽  
Peak Current ◽  
Powder Concentration ◽  
Pulse On Time

Electrical Discharge Machining (EDM) is extensively used to manufacture different conductive materials, including difficult to machine materials with intricate profiles. Powder Mixed Electro-Discharge Machining (PMEDM) is a modern innovation in promoting the capabilities of conventional EDM. In this process, suitable materials in fine powder form are mixed in the dielectric fluid. An equal percentage of graphite and silicon carbide powders have been mixed together with the transformer oil and used as the dielectric media in this work. The aim of this study is to investigate the effect of some process parameters such as peak current, pulse-on time, and powder concentration of machining High-speed steel (HSS)/(M2) on the material removal rate (MRR), tool wear rate (TWR) and the surface roughness (Ra). Experiments have been designed and analyzed using Response Surface Methodology (RSM) approach by adopting a face-centered central composite design (FCCD). It is found that added graphite-silicon carbide mixing powder to the dielectric fluid enhanced the MRR and Ra as well as reduced the TWR at various conditions. Maximum MRR was (0.492 g/min) obtained at a peak current of (24 A), pulse on (100 µs), and powder concentration (10 g/l), minimum TWR was (0.00126 g/min) at (10 A, 100 µs, and 10 g/l), and better Ra was (3.51 µm) at (10 A, 50 µs, and 10 g/l).

EFFECT OF METALLIC FUEL NATURE ON COMBUSTION CHARACTERISTICS OF AMMONIUM PERCHLORATE / BINDER / METAL COMPOSITE SOLID PROPELLANTS

2020 ◽  
Author(s):  
V. A. Poryazov ◽  
◽  
O. G. Glotov ◽  
V. A. Arkhipov ◽  
G. S. Surodin ◽  
...  
Keyword(s):  
Ammonium Perchlorate ◽  
Combustion Process ◽  
Experimental Information ◽  
Combustion Characteristics ◽  
Transformer Oil ◽  
Metal Composite ◽  
Butadiene Rubber ◽  
Solid Propellants ◽  
Metallic Fuel ◽  
Composite Solid Propellants

The goal of this research is to obtain experimental information about combustion characteristics of the composite propellant containing various metallic fuels. The propellant formulations contained two fractions of ammonium perchlorate (64.6%), inert binder (19.7%) - butadiene rubber SKD plastized with transformer oil, and metal fuel (15.7% of aluminum ASD-4, ASD-6, Alex; boron; aluminum diboride; aluminum dodecaboride; some mixtures of above listed ingredients). Experimental information will be used further as a background to develop the physical and mathematical model of combustion process.

Impact of Concentration of Nanoparticles on Characteristics of Transformer Oil

2020 ◽  
Vol 12 (4) ◽  
pp. 04019-4-04019-4
Author(s):  
Sumanta Kumar Tripathy ◽  
◽  
M. Madeen Kumar ◽  
Keyword(s):  
Transformer Oil

FURTHER CONTRIBUTION FOR EVALUATING THE AGING OF TRANSFORMER OIL OF POWER TRANSFORMER

2015 ◽  
Vol 43 (2) ◽  
pp. 211-226
Author(s):  
Sobhy S. Dessouky ◽  
Adel El Faraskoury ◽  
Sherif Ghoneim ◽  
Ahmed Haassan
Keyword(s):  
Power Transformer ◽  
Transformer Oil

Feasibility of the novel utilization of modified Azadirachta Indica seed oil as transformer oil

2020 ◽  
Author(s):  
Chinedu Agu ◽  
Matthew Menkiti ◽  
Albert Agulanna ◽  
Emeka Udokporo
Keyword(s):  
Azadirachta Indica ◽  
Seed Oil ◽  
Transformer Oil ◽  
The Novel

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.

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