Transformer winding faults classification based on transfer function analysis by support vector machine

Bigdeli, Mehdi; Vakilian, Mehdi; Rahimpour, Ebrahim

doi:10.1049/iet-epa.2011.0232

Cited by 124 publications

(79 citation statements)

References 37 publications

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“…spiral tightening, conductor tilting, radial/hoop buckling, shorted or open turns, loosened clamping structures, axial displacement, core movement, and collapse of the winding end supports [7,8]. Frequency response analysis (FRA) is known as the most reliable non-invasive diagnostic technique currently used to identify incipient winding deformation within power transformers [9,10]. Transformer components such as windings, core and insulation can be presented by equivalent electrical parameters comprising resistors, capacitors, and self / mutual inductances, whose values are altered due to any winding deformation leading to a change in the frequency response of the relevant equivalent circuit.…”

Section: Introductionmentioning

confidence: 99%

Improved power transformer winding fault detection using FRA diagnostics – part 1: axial displacement simulation

Hashemnia

Abu-Siada

Islam

2015

IEEE Trans. Dielect. Electr. Insul.

130

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Frequency response analysis (FRA) has become a widely accepted tool to detect power transformer winding deformation due to the development of FRA test equipment. Because FRA relies on graphical analysis, interpretation of its signature is a very specialized area that calls for skilled personnel, as so far, there is no reliable standard code for FRA signature identification and quantification. Many researchers investigated the impact of various mechanical winding deformations on the transformer FRA signature using simulation analysis by altering particular electrical parameters of the transformer equivalent electrical circuit. None of them however, investigated the impact of various physical fault levels on the corresponding change in the equivalent circuit parameters. In this paper, the physical geometrical dimension of a single-phase transformer is simulated using 3D finite element analysis to emulate the real transformer operation. A physical axial displacement of different fault levels is simulated in both low voltage and high voltage windings. The impact of each fault level on the electrical parameters of the equivalent circuit is investigated. A key contribution of this paper is the charts it introduces to correlate various axial displacement levels with the percentage change of all transformer equivalent circuit parameters due to the axial displacement fault. In contrary with other researchers who only considered mutual inductance between low voltage and high voltage windings, simulation resultsshown in this paper reveal that other circuit parameters should be changed by a particular percentage to accurately simulate particular fault level of transformer winding axial displacement. Results of this paper aid to precisely simulating winding axial displacement using transformer equivalent circuit that facilitates accurate qualitative and quantitative analysis of transformer FRA signatures.

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Section: Introductionmentioning

confidence: 99%

Improved power transformer winding fault detection using FRA diagnostics – part 1: axial displacement simulation

Hashemnia

Abu-Siada

Islam

2015

IEEE Trans. Dielect. Electr. Insul.

130

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“…Great research effort has been devoted to the discrimination between fault current and inrush current [9][10][11][12][13]. Recently, the data-dependent methods, such as support vector machine (SVM) [14,15], artificial neural network (ANN) [16,17], and random forest (RF) [13], have become more and more popular in tasks of discrimination between inrush current and fault current. These methods treat the discrimination problem as a classification problem and input data are used to satisfy the accuracy requirement.…”

Section: Introductionmentioning

confidence: 99%

Automatic J–A Model Parameter Tuning Algorithm for High Accuracy Inrush Current Simulation

Wen

Jing-zhuo

2017

Energies

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Inrush current simulation plays an important role in many tasks of the power system, such as power transformer protection. However, the accuracy of the inrush current simulation can hardly be ensured. In this paper, a Jiles-Atherton (J-A) theory based model is proposed to simulate the inrush current of power transformers. The characteristics of the inrush current curve are analyzed and results show that the entire inrush current curve can be well featured by the crest value of the first two cycles. With comprehensive consideration of both of the features of the inrush current curve and the J-A parameters, an automatic J-A parameter estimation algorithm is proposed. The proposed algorithm can obtain more reasonable J-A parameters, which improve the accuracy of simulation. Experimental results have verified the efficiency of the proposed algorithm.

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“…Many literatures have been reviewed for research proposed. Algorithm for fault diagnosis such as neural network (1)(2) , fuzzy logic (3)(4) , support vector machine (5)(6)(7)(8) , and etc. has been used in many researches.…”

Section: Introductionmentioning

confidence: 99%

Selection of Proper Non-linear Kernel Parameter in Support Vector Machine Algorithm for Classifying the Internal Fault Type in Winding Power Transformer

Klomjit¹,

Thongsuk²,

Ngaopitakkul

2014

Proceedings of the 2nd International Conference on Intelligent Systems and Image Processing 2014

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This paper proposes the proper kernel function in a support vector machine (SVM), which is used to classify the internal fault type in power transformer. The Gaussian kernel and polynomial kernel that are two types of non-linear kernel parameter are compared in terms of the average accuracy and time of training process. The results are shown that the polynomial kernel parameter of SVM algorithm is able to classify the internal fault type with satisfactory accuracy, and it takes the average time less than the other. The benefit of using polynomial kernel can be applied for fault diagnosis in future.

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Transformer winding faults classification based on transfer function analysis by support vector machine

Cited by 124 publications

References 37 publications

Improved power transformer winding fault detection using FRA diagnostics – part 1: axial displacement simulation

Improved power transformer winding fault detection using FRA diagnostics – part 1: axial displacement simulation

Automatic J–A Model Parameter Tuning Algorithm for High Accuracy Inrush Current Simulation

Selection of Proper Non-linear Kernel Parameter in Support Vector Machine Algorithm for Classifying the Internal Fault Type in Winding Power Transformer

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