Classifying Transformer Winding Deformation Fault Types and Degrees Using FRA Based on Support Vector Machine

Liu, Jiangnan; Zhao, Zhongyong; Tang, Chao; Yao, Chenguo; Li, Chengxiang; Islam, Syed

doi:10.1109/access.2019.2932497

Cited by 86 publications

(45 citation statements)

References 25 publications

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“…To simplify the assessment of hundreds of power transformer data, a Machine Learning-based DPM has been developed using the tool provided by MATLAB. The use of machine learning in power transformer assessment has also been reported by several studies [17,[37][38][39][40][41][42][43][44][45].…”

Section: Svm Model For Duval Pentagonmentioning

confidence: 76%

A Fuzzy Logic Model for Power Transformer Faults’ Severity Determination Based on Gas Level, Gas Rate, and Dissolved Gas Analysis Interpretation

et al. 2020

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In determining the severity of power transformer faults, several approaches have been previously proposed; however, most published studies do not accommodate gas level, gas rate, and Dissolved Gas Analysis (DGA) interpretation in a single approach. To increase the reliability of the faults’ severity assessment of power transformers, a novel approach in the form of fuzzy logic has been proposed as a new solution to determine faults’ severity using the combination of gas level, gas rate, and DGA interpretation from the Duval Pentagon Method (DPM). A four-level typical concentration and rate were established based on the local population. To simplify the assessment of hundreds of power transformer data, a Support Vector Machine (SVM)-based DPM with high agreements to the graphical DPM has been developed. The proposed approach has been implemented to 448 power transformers and further implementation was done to evaluate faults’ severity of power transformers from historical DGA data. This new approach yields in high agreement with the previous methods, but with better sensitivity due to the incorporation of gas level, gas rate, and DGA interpretation results in one approach.

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Section: Svm Model For Duval Pentagonmentioning

confidence: 76%

A Fuzzy Logic Model for Power Transformer Faults’ Severity Determination Based on Gas Level, Gas Rate, and Dissolved Gas Analysis Interpretation

et al. 2020

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“…If this is compared with the frequency response of the single air core LV winding, it seems the first pseudo anti-resonant frequency has been shifted to a lower frequency due to the introduction of the magnetizing inductance, and there is no change in the other resonances. The so called 'U' shape at the core dominant low frequency region has been stated in [17,20] as "…in the intermediate frequency band (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), with the apparent absence of the usual first low frequency minima and maxima", and "… is not unique to shell form transformers, being occasionally seen for some core form units, e.g. delta connected HV windings".…”

Section: B Typical Frequency Response Of Single Winding With Magnetimentioning

confidence: 99%

“…Statistical indices were used to objectively assess the differences in the FRA spectra measured before and after the fault [1,2], as produced by either laboratory experiments [3,4] or simulation studies [5][6][7][8]. Artificial intelligence (AI) was also used to process the FRA data to identify winding faults [9,10]. Nevertheless, a quantitative relationship linking winding geometry, equivalent electrical components, and the corresponding FRA measurement results is still desirable.…”

Section: Introductionmentioning

confidence: 99%

Using Lumped Element Equivalent Network Model to Derive Analytical Equations for Interpretation of Transformer Frequency Responses

2020

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Frequency Response Analysis (FRA) is regarded as the most effective technique for detecting mechanical faults in transformer windings. However, the main challenge in applying the FRA technique lies in the correct interpretation of the differences between the diagnosis and the reference frequency spectra. A transformer can be modelled as an equivalent circuit network comprising inductances (L) and capacitances (C), where L and C are electrical parameters dependent on the winding geometry; a relationship exists between the winding geometry, the electrical components of the equivalent circuit network and the measured FRA spectra. Through electromagnetic circuit analysis, this paper provides a theoretical basis to develop a fundamental understanding of FRA spectra and their resulting interpretation. The FRA spectra are first explained for a single air core winding with uniform structure, and then the complexity is gradually increased by taking into consideration the iron core, the mutual inductive coupling between parts of the same winding and then between the separate windings of the transformer. Mathematical expressions are developed to represent the relationship between the FRA characteristics and the corresponding electrical components of the equivalent circuit network model. INDEX TERMS Transformer Modelling, Frequency Response Analysis (FRA), Interpretation, Electrical Circuit Analysis NOMENCLATURE Real part of permeability of electrical steel in per unit value Imaginary part of permeability of electrical steel in per unit value Winding series capacitance Winding shunt capacitance to ground α Space coefficient Magnetizing inductance of winding Air core inductance of winding Magnetic coupling factor between parts of winding with iron core present Magnetic coupling factor between parts of air core winding Equivalent series capacitance of winding seen from tested winding terminals in a transformer Equivalent winding shunt capacitance to ground seen from tested winding terminals in a transformer n Number of stages/sections in a lumped element equivalent circuit network model Angular frequency N Voltage ratio of high voltage to low voltage windings of transformer

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“…Such challenges were addressed and, the usefulness of feature extractions from FRA results in the form of statistical tools was successfully demonstrated in recent researches [19]- [21]. Jiangnan Liu et al combined the Support Vector Machine with FRA to diagnose the transformer faults and, discussed the usefulness of the parameter optimisation algorithms [22].…”

Section: Introductionmentioning

confidence: 99%

Influence of the Load on the Impulse Frequency Response Approach Based Diagnosis of Transformer’s Inter-Turn Short-Circuit

2020

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Frequency Response Analysis (FRA) is an Off-line condition monitoring technique for the transformers and is successful, in diagnosing mechanical faults and inter-turn short-circuits. Only limited literature is available for On-line FRA works, and the effect of the transformer's load on the fault diagnosing capability of FRA has not yet been analysed. Motivated by this research gap, an experimental investigation was done to analyze the effect of load on IFRA based inter-turn short-circuit diagnosis. A single-phase 1 kVA, 240V/240 V transformer supplying R and R-L loads and a three-phase 5 kVA, 440 V/440 V transformer supplying an induction motor at various load levels were tested. Inter-turn short-circuits were emulated in their windings and, the effectiveness of IFRA in diagnosing the same was investigated. Comparisons were made between the frequency response of the loaded transformers in their healthier conditions and emulated fault conditions, based on the transfer function plots and the statistical parameters. Investigations reveal that the loads alter the useful spectrum of IFRA. The frequency response of the healthier transformer itself varies based on the transformer's load and necessitates a careful focus on the sensitive frequency spectra for effective inter-turn short-circuit diagnosis in loaded transformers.

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Classifying Transformer Winding Deformation Fault Types and Degrees Using FRA Based on Support Vector Machine

Cited by 86 publications

References 25 publications

A Fuzzy Logic Model for Power Transformer Faults’ Severity Determination Based on Gas Level, Gas Rate, and Dissolved Gas Analysis Interpretation

A Fuzzy Logic Model for Power Transformer Faults’ Severity Determination Based on Gas Level, Gas Rate, and Dissolved Gas Analysis Interpretation

Using Lumped Element Equivalent Network Model to Derive Analytical Equations for Interpretation of Transformer Frequency Responses

Influence of the Load on the Impulse Frequency Response Approach Based Diagnosis of Transformer’s Inter-Turn Short-Circuit

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