A probabilistic neural network classifier-based method for transformer winding fault identification through its transfer function measurement

Bigdeli, Mehdi; Vakilian, Mehdi; Rahimpour, Ebrahim

doi:10.1002/etep.668

Cited by 19 publications

(7 citation statements)

References 34 publications

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“…The first category includes methods according to which faults' classification is solely based on the rate of variations in numerical indices (statistical and mathematical indices) in specific frequency ranges [9]- [18]. The second category includes methods that use intelligent classifiers to distinguish faults [19]- [25]. In these methods, the necessary features of frequency response (mainly the statistical and numerical indices) are extracted, and these features are used for training and testing classifiers.…”

Section: Introductionmentioning

confidence: 99%

“…In [19], [20], based on the TF estimation with the help of vector fitting, the necessary features of the measured TFs for four faults AD, RD, DSV, and SC were extracted and these faults are classified by using PNN [19] and SVM [20]. A distinction has been made between electrical and mechanical faults (AD and RD only) and the inrush current through ANN and the DT in [21].…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Classifiers in Distinguishing Transformer Faults Using Frequency Response Analysis

2021

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With the expansion of the use of frequency response analysis (FRA) as a reliable tool for fault detection in transformers, more capabilities of this method are discovered every day. So that today the number of transformer faults that can be identified by FRA method has also increased. One of the most critical steps in fault detection with FRA is to distinguish faults and classify them in different classes. In this paper, well-known intelligent classifiers (probabilistic neural network, decision tree, support vector machine, and k-nearest neighbors) are used to classify transformer faults. For this purpose, the necessary measurements are performed on the model transformers under the healthy condition and under different fault conditions (axial displacement, radial deformation, disc space variation, short-circuits, and core deformation). Then, by dividing the frequency ranges of the measured transfer functions of the transformer, a new feature based on numerical and statistical indices for training and validation of classifiers is proposed. After completing the training process, the performance of the classifiers is evaluated and compared by applying the data obtained from real transformers. INDEX TERMS Transformer, fault type detection, frequency response analysis (FRA), intelligent classifiers, measurement, numerical indices.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Classifiers in Distinguishing Transformer Faults Using Frequency Response Analysis

2021

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“…One approach to interpreting FRA data has been the application of Black Box modelling [17][18][19]. This approach is based on the development of mathematical models which accurately represent the frequency response between various terminal combinations.…”

Section: Introductionmentioning

confidence: 99%

“…Any change in the FRA will result in model parameter changes which can then be interpreted for indications of a structural change within the transformer. Researchers such as Bigdeli [18, 19] have used neural networks to classify the fault relative to the change in parameters. Such an approach is dependent upon training data for different transformer topologies and their corresponding fault conditions.…”

Section: Introductionmentioning

confidence: 99%

Methodology to locate and quantify radial winding deformation in power transformers

Mitchell

Welsh

2017

High Voltage

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Frequency response analysis (FRA) is a tool that can be used to detect changes within the structural geometry of a transformer. One application of FRA is to detect winding displacement after the transformer experiences an overcurrent event. This paper proposes a methodology which will facilitate both locating and quantifying winding deformation. The procedure is based on the fitting of a Gray Box transformer model to FRA measurements which were recorded both before and after the fault. Subtle variation in key parameters of the model can then be used to quantify winding deformation severity. The research will demonstrate the applicability of this approach to a wide range of transformer applications by using FRA from a modified 1.3 MVA distribution transformer, and assessing the change in key parameters relative to the emulated buckling introduced to the windings. The authors propose that this approach is ideally suited for use within an automated diagnostic system which will locate, diagnose and quantify winding deformation within power transformers. High Voltage

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“…At high current values, these induced currents cause overheating of the metal structure, which can degrade the properties of transformer oil, damage the painting of the tank, and damage the insulation of nearby cables . All these harms can be a cause of major fault in power transformers .…”

Section: Introductionmentioning

confidence: 99%

Fast computation of hot spots temperature due to high current cable leads in power transformers tank walls

Magdaleno-Adame¹,

Olivares-Galván

Penabad‐Duran

et al. 2014

Int. Trans. Electr. Energ. Syst.

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Summary This paper presents a set of analytical functions employed to compute the temperature in tank walls produced by High Current Cable Leads (HCCLs) in core‐type power transformers. The functions presented in this paper are deduced from 3D Finite Element Method (FEM) simulations. Numerous FEM simulations are performed to calculate the temperature in real carbon steel tank walls with and without aluminum shields. Design parameters are modified within a realistic range using a 3D FEM parametric methodology. Temperature curves and temperature functions are obtained from numerical results. For the validation of the approach presented in this paper, transformers are tested in the laboratory, and measurements are compared with analytical results from the proposed temperature functions. Copyright © 2014 John Wiley & Sons, Ltd.

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A probabilistic neural network classifier-based method for transformer winding fault identification through its transfer function measurement

Cited by 19 publications

References 34 publications

Intelligent Classifiers in Distinguishing Transformer Faults Using Frequency Response Analysis

Intelligent Classifiers in Distinguishing Transformer Faults Using Frequency Response Analysis

Methodology to locate and quantify radial winding deformation in power transformers

Fast computation of hot spots temperature due to high current cable leads in power transformers tank walls

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