MODELING OFDISTRIBUTION TRANSFORMERS BY A 3-D NUMERICAL TECHNIQUE

2020 ◽  
pp. 56-62
Author(s):  
Vuong
Keyword(s):  
Magnetic Flux ◽  
Numerical Technique ◽  
Short Circuit ◽  
Electrical Equipment ◽  
Cover Plate ◽  
Magnetic Vector Potential ◽  
Practical Application ◽  
Leakage Flux ◽  
Electromagnetic Quantities ◽  
Flux Leakage

Modeling of electromagnetic quantities (magnetic flux, leakage flux, eddy current loss, and electromagnetic force) in distributed power transformers remarkably plays an essential role for researchers and designers in the calculation and production of electrical equipment in general and transformer designs in particular. In the frame of this research, the distribution of magnetic flux densities along the tank wall and cover plate, the leakage flux in air regions, and the forces for both rated and short circuit modes in the windings of the transformer are computed/simulated via a 3-D numerical method. The method is herein developed with magnetic vector potential formulations and is applied to a practical application of the distributed transformer (630kVA-22/0,4kV).

scholarly journals Location Detection Method of Detector in Pipeline Using VMD Algorithm and Machine Learning Classifier

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1436
Author(s):  
Tuoru Li ◽  
Senxiang Lu ◽  
Enjie Xu
Keyword(s):  
Machine Learning ◽  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Intrinsic Mode Functions ◽  
Detection Range ◽  
Vibration Signals ◽  
Feature Vectors ◽  
Learning Classifier ◽  
Mode Decomposition ◽  
Flux Leakage

The internal detector in a pipeline needs to use the ground marker to record the elapsed time for accurate positioning. Most existing ground markers use the magnetic flux leakage testing principle to detect whether the internal detector passes. However, this paper uses the method of detecting vibration signals to track and locate the internal detector. The Variational Mode Decomposition (VMD) algorithm is used to extract features, which solves the defect of large noise and many disturbances of vibration signals. In this way, the detection range is expanded, and some non-magnetic flux leakage internal detectors can also be located. Firstly, the extracted vibration signals are denoised by the VMD algorithm, then kurtosis value and power value are extracted from the intrinsic mode functions (IMFs) to form feature vectors, and finally the feature vectors are input into random forest and Multilayer Perceptron (MLP) for classification. Experimental research shows that the method designed in this paper, which combines VMD with a machine learning classifier, can effectively use vibration signals to locate the internal detector and has the characteristics of high accuracy and good adaptability.

Alternative magnetic flux leakage modalities for pipeline inspection

1996 ◽  
Vol 32 (3) ◽  
pp. 1581-1584 ◽  
Author(s):  
G. Katragadda ◽  
W. Lord ◽  
Y.S. Sun ◽  
S. Udpa ◽  
L. Udpa
Keyword(s):  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Pipeline Inspection ◽  
Flux Leakage

Research on Quantitative Method and 3D Finite Element Analysis of Tank Bottom Corrosion Defect Leakage Magnetic Field

2011 ◽  
Author(s):  
Guang Dai ◽  
Wei Li ◽  
Zhijun Yang ◽  
Yali Wang
Keyword(s):  
Finite Element ◽  
Magnetic Flux ◽  
Quantitative Method ◽  
Defect Size ◽  
Magnetic Signal ◽  
3D Finite Element ◽  
Leakage Magnetic Field ◽  
Tank Bottom ◽  
The Relationship ◽  
Flux Leakage

According to the principle of magnetic flux leakage testing and the finite element numerical simulation, 3D finite element model (FEM) for MFL testing of tank bottom was established. Through simulative analysis, influence law between defect size and defect magnetic flux leakage field and the relationship curve of defect leakage magnetic field change with its size was obtained. The result showed that: When the length vary with sequence, the peak valley length of defects leakage magnetic signal increase with the increase of defect length, the relationship curve between each other is approximate linear; When the depth vary with sequence, the relationship between peak valley height of defects leakage magnetic signal and defect depth is also approximate linear, but this relationship was different with different length; When the width vary with sequence, on the defect symmetry plane, the peak of magnetic flux density along the width direction corresponded with the defect edge, and the length of the two peaks were equal to the width of the defect. According to simulation data and theory of regression analysis linear regression equation and relation surface between defect length depth and the characteristic quantity of its signal was established, quantitative method of defects signal was also proposed. Then experimental study was carried out in the Laboratory, the result show that the quantitative size was consistent with the actual defect size.

GMR-based magnetic flux leakage technique for condition monitoring of steel track rope

2011 ◽  
Vol 53 (7) ◽  
pp. 377-381 ◽  
Author(s):  
W Sharatchandra Singh ◽  
B P C Rao ◽  
C K Mukhopadhyay ◽  
T Jayakumar
Keyword(s):  
Magnetic Flux ◽  
Condition Monitoring ◽  
Magnetic Flux Leakage ◽  
Flux Leakage
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