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

Shamlou, Alireza; Feyzi, Mohammad Reza; Behjat, Vahid

doi:10.1049/iet-gtd.2019.0320

Cited by 10 publications

(7 citation statements)

References 34 publications

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“…1 As a sensitive and economic technique, frequency response analysis (FRA) method has been largely employed to assess the inner mechanical and electrical conditions of a winding. [2][3][4][5][6][7][8][9] The basic principle of FRA measurement is to inject a series of low-voltage sweep frequency signals into one terminal of a winding and measure the response voltage signals from the other terminal of the winding with the untested winding left floating. By plotting the ratio of the response voltage to the input voltage against sweep frequencies, FRA curve of the winding can be obtained.…”

Section: Introductionmentioning

confidence: 99%

“…A survey on failures in power transformers shows that winding failures account for a large proportion 1 . As a sensitive and economic technique, frequency response analysis (FRA) method has been largely employed to assess the inner mechanical and electrical conditions of a winding 2–9 . The basic principle of FRA measurement is to inject a series of low‐voltage sweep frequency signals into one terminal of a winding and measure the response voltage signals from the other terminal of the winding with the untested winding left floating.…”

Section: Introductionmentioning

confidence: 99%

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Cut‐set matrix methods for frequency response analysis curves of lumped parameter equivalent circuit model of three‐winding power transformer

Wang,

Xie

et al. 2023

Circuit Theory & Apps

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SummaryTo accurately and efficiently calculate the frequency response of the lumped parameter equivalent circuit model (LPECM) of a transformer winding is the basis for studying the simulation method of the frequency response analysis (FRA). In this paper, two different cut‐set matrix (CSM) methods, including fundamental cut‐set (FCS) and independent cut‐set (ICS) matrix methods, are proposed to calculate the FRA curves of the LPECM of a three‐winding power transformer. First, based on graph theory and electrical circuit laws, the basic CSM equation is deduced. Second, four different FCS matrix selection methods of the LPECM are introduced and the best FCS matrix is determined by deeply analyzing their characteristics. Then, an ICS matrix method is proposed to obtain the ICS of the LPECM. Furthermore, FCS and ICS are taken into the CSM equation to calculate the FRA results of the LPECM, respectively. Finally, the accuracy of the proposed methods is validated by Multisim software and the performance of these two methods is compared with node voltage matrix (NVM) method by analyzing the calculation time. The results show that FCS and ICS matrix methods both can be used to accurately obtain the FRA results of the LPECM of the three‐winding power transformer, but ICS is more simple than FCS matrix method due to avoiding the procedure for the selection of the tree. In addition, since ICS matrix method has the shortest running time among these methods, it can be used to improve the calculation efficiency of the FRA curves of the LPECM of a three‐winding power transformer.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cut‐set matrix methods for frequency response analysis curves of lumped parameter equivalent circuit model of three‐winding power transformer

Wang,

Xie

et al. 2023

Circuit Theory & Apps

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“…Unfortunately, the existing standards in the field of FRA [2], [3] have focused on measurement requirements and test circuits, and a precise standard for interpreting FRA measurement results has not yet been developed. Therefore, in recent years, many studies have been conducted on the interpretation of FRA measurement's results to obtain information about transformer faults [4]- [8]. Given that the first step is to identify the type of fault, the focus of current research is to classify 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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“…It has been recommended that the four indices including Lin’s concordance coefficient (LCC), standard difference area (SDA), the sum of errors (SE) and cross correlation factor (CCF) are as the most suitable ones for determining the degree of change between two frequency responses compared with other indices. Evidence theory was introduced in Shamlou et al (2019) to detect the existence of a defect in the windings of an autotransformer. This method could not diagnose the fault type.…”

Section: Introductionmentioning

confidence: 99%

Generalized regression neural network application for fault type detection in distribution transformer windings considering statistical indices

Behkam

Karami

Naderi

et al. 2021

COMPEL

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Purpose This study aims to use frequency response analysis, a powerful tool to detect the location and types of transformer winding faults. Proposing an effective intelligent approach for interpreting the frequency responses is the most crucial problem of this method and has created many challenges. Design/methodology/approach Heat maps based on appropriate statistical indices have been supplied to depict the variations in the frequency responses associated with each fault type, fault location and fault extent along the windings. Also, after analyzing the results of artificial neural network (ANN) techniques, the generalized regression neural network method is introduced as the most effective solution for the classification of transformer winding faults. Findings Using a comparative approach, the performance of the used indices and ANN techniques are evaluated. The results showed the proper performance of Lin’s concordance coefficient (LCC) index and the amplitude (Amp) part of the frequency response. The proposed fitting percentage (FP) index can assist the intelligent classifiers in diagnosing the radial deformation (RD) fault with the highest accuracy considering all frequency response components in the classification procedure of winding faults. Practical implications Various ANN techniques are used to detect and determine the type of four important faults of transformer winding, i.e. axial displacement, RD, disc space variation and short circuit. Various statistical indices, such as cross-correlation factor, LCC, standard difference area, sum of errors, normalized root-mean-square deviation and FP, are used to extract the features of the frequency responses to consider as the ANN inputs. In addition, different components of the frequency response, such as Amp, argument, real and imaginary parts are examined in this paper. To implement the proposed procedure, step by step, various types of winding faults with different locations and extents are applied on the 20 kV winding of a 1.6 MVA distribution transformer. Originality/value Contributions have been made in identifying and diagnosing transformer winding defects through the use of appropriate algorithms for future research.

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

Cited by 10 publications

References 34 publications

Cut‐set matrix methods for frequency response analysis curves of lumped parameter equivalent circuit model of three‐winding power transformer

Cut‐set matrix methods for frequency response analysis curves of lumped parameter equivalent circuit model of three‐winding power transformer

Intelligent Classifiers in Distinguishing Transformer Faults Using Frequency Response Analysis

Generalized regression neural network application for fault type detection in distribution transformer windings considering statistical indices

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