Diagnosing Transformer Winding Deformation Faults Based on the Analysis of Binary Image Obtained From FRA Signature

Zhao, Zhongyong; Yao, Chenguo; Tang, Chao; Li, Chengxiang; Yan, Fayou; Islam, Syed

doi:10.1109/access.2019.2907648

Cited by 31 publications

(16 citation statements)

References 28 publications

(33 reference statements)

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“…However, the efficiency of combination algorithm in detecting fault types and severities is obvious. Table 6 shows the comparison between the proposed method in this paper and the latest published method [34]. As shown in this table, the outstanding feature of the Dempster-Shafer method is that there is no need for external classifiers such as SVM.…”

Section: Proposed Algorithm Performance In Diagnosing Axial and Radiamentioning

confidence: 99%

Interpretation of frequency response analysis of power transformer based on evidence theory

Shamlou

Feyzi

Behjat

2019

IET Generation, Transmission & Distribution

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Section: Proposed Algorithm Performance In Diagnosing Axial and Radiamentioning

confidence: 99%

Interpretation of frequency response analysis of power transformer based on evidence theory

Shamlou

Feyzi

Behjat

2019

IET Generation, Transmission & Distribution

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“…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]. Classification of RD, DSV, and SC faults in three different classes has been done in [22], [23]. In [22], the measured frequency response from the transformer is divided into three frequency ranges and using binary imaging and SVM, a distinction is made between the faults.…”

Section: Introductionmentioning

confidence: 99%

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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“…By using the DIP technique, the FRA polar plot image features can be extracted. There have been efforts to apply DIP to transformer fault diagnosis [24][25][26]. Ref.…”

Section: Introductionmentioning

confidence: 99%

Improved Winding Mechanical Fault Type Classification Methods Based on Polar Plots and Multiple Support Vector Machines

Liu

Zhao

Pang³

et al. 2020

IEEE Access

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The accurate and fast diagnosis of transformer winding deformation faults is of significance to power suppliers and utilities. An improved winding mechanical deformation fault classification method is proposed. In this study, the transformer frequency response data is used to draw polar plots, and then its texture features are extracted for fault classification. The classification model constructed by multiple support vector machines is successfully obtained and shows good classification effect. Besides, this paper uses an improved genetic algorithm based on the Emperor-Selective mating scheme and catastrophic operation, to optimize the parameters of support vector machine. The feasibility and accuracy of the proposed method are verified with experimental data obtained from a model transformer, and the proposed method is demonstrated to exhibit better performance compared with the traditional method.

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Diagnosing Transformer Winding Deformation Faults Based on the Analysis of Binary Image Obtained From FRA Signature

Cited by 31 publications

References 28 publications

Interpretation of frequency response analysis of power transformer based on evidence theory

Interpretation of frequency response analysis of power transformer based on evidence theory

Intelligent Classifiers in Distinguishing Transformer Faults Using Frequency Response Analysis

Improved Winding Mechanical Fault Type Classification Methods Based on Polar Plots and Multiple Support Vector Machines

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