Experimental verification of distribution transformer model under lightning strokes

2009 ◽  
Author(s):  
Nehmdoh A. Sabiha ◽  
Matti Lehtonen
Keyword(s):  
Experimental Verification ◽  
Distribution Transformer ◽  
Transformer Model

Building Factor of Grain-Oriented Silicon Iron (3% SiFe) with Different Thickness on 100kVA Three Phase Distribution Transformer Core

2012 ◽  
Vol 488-489 ◽  
pp. 537-541
Author(s):  
N. Ashbahani ◽  
I. Daut ◽  
Mohd Irwan Yusoff
Keyword(s):  
Power Loss ◽  
Phase Distribution ◽  
Operation Mode ◽  
Load Test ◽  
Core Material ◽  
Distribution Transformer ◽  
Silicon Iron ◽  
Three Phase ◽  
Transformer Model ◽  
Transformer Core

The power loss in laminated transformer cores is always greater than the nominal loss of the electrical steel laminations, by a factor known as the building factor. This paper discussed result of an investigation towards the effect of using two different Grain-Oriented Silicon Iron (3%SiFe) materials to the 100kVA three phase distribution transformer. The thicknesses of the material that have been used in this research are 0.23mm and 0.27mm. The transformer core will be assembled with 60o T-joint with 5mm mitred corner overlap length. Power loss has been measured using no-load test with 29 layer of lamination while nominal loss measured using Epstein test frame. At the operation mode flux density, 1.5T, the building factor of the transformer model core material with 0.23mm thickness is 1.219 while with the building factor for 0.27mm thickness is 1.250. This shows that thinner transformer core lamination is better than the other one by 2.5% during operation mode.

scholarly journals Distribution Transformer Parameters Detection Based on Low-Frequency Noise, Machine Learning Methods, and Evolutionary Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4332
Author(s):  
Daniel Jancarczyk ◽  
Marcin Bernaś ◽  
Tomasz Boczar
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Input Data ◽  
Learning Algorithms ◽  
Low Frequency ◽  
Machine Learning Algorithms ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
Distribution Transformer ◽  
Transformer Model

The paper proposes a method of automatic detection of parameters of a distribution transformer (model, type, and power) from a distance, based on its low-frequency noise spectra. The spectra are registered by sensors and processed by a method based on evolutionary algorithms and machine learning. The method, as input data, uses the frequency spectra of sound pressure levels generated during operation by transformers in the real environment. The model also uses the background characteristic to take under consideration the changing working conditions of the transformers. The method searches for frequency intervals and its resolution using both a classic genetic algorithm and particle swarm optimization. The interval selection was verified using five state-of-the-art machine learning algorithms. The research was conducted on 16 different distribution transformers. As a result, a method was proposed that allows the detection of a specific transformer model, its type, and its power with an accuracy greater than 84%, 99%, and 87%, respectively. The proposed optimization process using the genetic algorithm increased the accuracy by up to 5%, at the same time reducing the input data set significantly (from 80% up to 98%). The machine learning algorithms were selected, which were proven efficient for this task.

Evaluation and improvement of the main insulation structure of 33kV distribution transformer based on FEM

2019 ◽  
Vol 15 (1) ◽  
pp. 36
Author(s):  
Ibrahim Jalil Jebur ◽  
Kassim Rasheed Hameed
Keyword(s):  
Electric Field ◽  
Electric Field Intensity ◽  
Electric Field Distribution ◽  
Reference Data ◽  
Assessment Process ◽  
Fe Model ◽  
Distribution Transformer ◽  
Type Distribution ◽  
Transformer Model ◽  
Real Test

<span>This paper introduces a 2-D FE model to analyze the electric field distribution to evaluate and improve the insulation structure of the 250kVA, 33/0.4kV wound core type distribution transformer using the ANSYS 17.2 by implementing APDL program. The dielectric lighting impulse test was simulated in order to test the presented transformer model according to the IEC67006-3 standard, the assessment process is based on determining the electric stresses and compare it with the maximum permissible value of electric field intensity. Three proposed FE model as an improvement to the insulation structure of the transformer included reducing the insulation distances, changing the materials type, and mix both of them. The results obtained from the FE solution were compared with those of the real test, and presented in form of contours, curves, and vectors. The results obtained from the presented model can be further treated as a reference data in the design process.</span>

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