scholarly journals ANALYSIS ON DEGRADATION AND DEFORMATION OF TRANSFORMER INSULATION SYSTEM

2014 ◽  
pp. 75-81
Author(s):  
T. MANIVANNAN
Keyword(s):  
Frequency Response ◽  
Power Transformer ◽  
Good Condition ◽  
Dielectric Strength ◽  
Response Analysis ◽  
Insulation System ◽  
Distribution Transformers ◽  
Insulation Degradation ◽  
Transformer Winding ◽  
Transformer Insulation

Performance of insulation system of power transformer is essential to ensure better performance of power network. Most of the power and distribution transformers in service are aged and, have experienced several thermal and electrical stresses due to varying loading conditions, in addition to ageing. The rate of degradation and hence, the useful life of the insulation system mainly depends on the variation of operational stresses. Due to the progress in insulation degradation, the dielectric strength of the insulating oil and paper over transformer winding may reach to a point such that it could not withstand any small abnormal currents and voltages. In order to maintain the quality of system operation it is necessary to keep the transformer in good condition. This needs appropriate maintenance based on reliable diagnostics. In this paper experimental results on the application of frequency response analysis (FRA) for the diagnosis of power transformers. Frequency Response of Transformer, also serves for assessing the deformation process.

scholarly journals Frequency Response Modeling of Transformer Windings Utilizing the Equivalent Parameters of a Laminated Core

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2371 ◽  
Author(s):  
Konstanty Marek Gawrylczyk ◽  
Katarzyna Trela
Keyword(s):  
Frequency Response ◽  
Research Utilization ◽  
Power Transformer ◽  
Three Dimensional ◽  
Magnetic Core ◽  
Wide Frequency Range ◽  
Response Analysis ◽  
Response Curves ◽  
Transformer Winding ◽  
Response Modeling

The aim of the article is to present the method for modeling transformer winding inductance, taking into account the complex magnetic permeability and equivalent electric conductivity of the magnetic core. In the first stage of the research, a physical model of a 24-turn coil wound on the distribution transformer core was prepared. The Frequency Response Analysis (FRA) measurements of the coil were taken; then, the inductance of the coil as a function of frequency was calculated from the received frequency response curves. In the second stage, two-dimensional (2D) and three-dimensional (3D) computer models of the coil based on the finite element method (FEM) were established. In order to obtain the equivalent inductance characteristics of the winding modeled in 2D and 3D in a wide frequency range, the equality of the reluctance of the limbs and yokes in both models was assured. In the next stage of the research, utilization of the equivalent properties for the laminated magnetic material simulations was proposed. This outcome can be used to calculate the frequency response of the winding of the power transformer. The other obtained result is the method for modeling the resonance slope, which is visible on the inductance curve received from the FRA measurement.

scholarly journals Transformer Winding Condition Assessment Using Feedforward Artificial Neural Network and Frequency Response Measurements

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3227
Author(s):  
Mehran Tahir ◽  
Stefan Tenbohlen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Frequency Response ◽  
Power Transformer ◽  
Classification Model ◽  
Response Analysis ◽  
Transformer Winding ◽  
Artificial Neural ◽  
Electrical Faults ◽  
Artificial Neural Network Ann

Frequency response analysis (FRA) is a well-known method to assess the mechanical integrity of the active parts of the power transformer. The measurement procedures of FRA are standardized as described in the IEEE and IEC standards. However, the interpretation of FRA results is far from reaching an accepted and definitive methodology as there is no reliable code available in the standard. As a contribution to this necessity, this paper presents an intelligent fault detection and classification algorithm using FRA results. The algorithm is based on a multilayer, feedforward, backpropagation artificial neural network (ANN). First, the adaptive frequency division algorithm is developed and various numerical indicators are used to quantify the differences between FRA traces and obtain feature sets for ANN. Finally, the classification model of ANN is developed to detect and classify different transformer conditions, i.e., healthy windings, healthy windings with saturated core, mechanical deformations, electrical faults, and reproducibility issues due to different test conditions. The database used in this study consists of FRA measurements from 80 power transformers of different designs, ratings, and different manufacturers. The results obtained give evidence of the effectiveness of the proposed classification model for power transformer fault diagnosis using FRA.

scholarly journals Fault location using Sweep Frequency Response Analysis in Disc type transformer winding

2015 ◽  
pp. 167-170
Author(s):  
Jineeth Joseph ◽  
Usha K ◽  
Usa. S.
Keyword(s):  
Frequency Response ◽  
Fault Location ◽  
Power Transformer ◽  
Circuit Simulation ◽  
Response Analysis ◽  
Sweep Frequency ◽  
Irreversible Damage ◽  
Transformer Winding ◽  
Frequency Response Analyzer ◽  
Two Parameters

Winding faults are the leading cause of power transformer failures. Usually these failures develop in to more serious faults that would result in irreversible damage to the transformer winding and the consequential costs. This contribution is aimed at locating the faults by defining a set of two parameters based on change in impedance due to fault in the winding. Modeling and measurement of 22 kV continuous disc winding is done using circuit simulation package and sweep frequency response analyzer respectively.. The identified parameters are used in localization of faults in the transformer winding. The proposed method for localization of fault by measurement is validated by modeling.

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