Fault severity determination in transformers using dissolved gas analysis(DGA)

Wani, Shufali Ashraf; Farooque, Md. Umer; Khan, Shakeb A.; Gupta, Dhawal; Khan, M. Ajmal

doi:10.1109/indicon.2015.7443362

Cited by 19 publications

(14 citation statements)

References 13 publications

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“…The transformer oil decomposition leading to the formation of fault gases is studied through a decomposition model involving an n-octane molecule. The n-octane molecule is selected because it possesses paraffinic traits and properties similar to those compounds that would decompose to produce the fault gases [24][25][26].…”

Section: Dga and The Concept Of Weighted Dgamentioning

confidence: 99%

“…The input to the ANN is again the key gases, which have been weighted appropriately by a weighting factor. This weighting factor is a constant value and is dependent on the amount of energy required to produce a particular fault gas [26]. The outputs are the different fault conditions that may exist in the transformer (F1-F8) and one no-fault condition (F0).…”

Section: Ewdga Using Annmentioning

confidence: 99%

“…The concentration of the various gases formed depends on the energy content of the fault or severity of fault [24][25][26]. However, most fault diagnosis studies do not take into account the fault energy criteria for fault identification.…”

Section: Introductionmentioning

confidence: 99%

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Transformer incipient fault diagnosis on the basis of energy-weighted DGA using an artificial neural network

Equbal¹,

Khan²,

Islam³

2018

Turk J Elec Eng & Comp Sci

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Abstract:In this paper, a transformer incipient fault diagnosis model has been developed with the help of an artificial neural network (ANN), taking into account the difference in the energy required to produce the different fault gases.The key fault gases are indicative of the fault type prevailing in the transformer. However, in conventional studies, the energy difference in fault gas formation is not considered while adopting the key gas method for fault diagnosis. In this work, a weighting factor has been used to take into account this relative difference in energy requirement for various fault gas formations. The fault gas concentrations have been suitably weighted by their respective weighting factors before being used in the incipient fault diagnosis process. A backpropagation ANN has been appropriately trained using the weighted fault gas concentration for transformer incipient fault identification. The model has been trained to identify fault types as enlisted in the transformer fault-interpreting standard IEC-599. The developed ANN model has been tested for its diagnostic capability using a reported fault database. The comparative diagnosis results presented here show clear improvement in the diagnosis of transformer internal faults using the energy-weighted ANN model over the unweighted ANN model.

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Section: Dga and The Concept Of Weighted Dgamentioning

confidence: 99%

Section: Ewdga Using Annmentioning

confidence: 99%

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Transformer incipient fault diagnosis on the basis of energy-weighted DGA using an artificial neural network

Equbal¹,

Khan²,

Islam³

2018

Turk J Elec Eng & Comp Sci

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“…For D1 and D 2 , EWMEA is 2.7 times to that of un‐weighted MEA. Thus, severity of electrical faults is greater than thermal faults (D 2 > D1 > T 3 > T 1 and T2) which is clearly reflected by their energy weighted methane, ethylene, and acetylene (EWMEA) …”

Section: Severity Of Duval Triangle Fault Regionsmentioning

confidence: 99%

“…The faults are indexed between the range 0 to 15 and 3 severity levels are rated on the scale of 0 to 9 as under in Table . Rule base: The input output mapping is done using a set of 108 if‐else rules. These rules are framed on the basis of Duval triangle method . An example of formulated rule‐base is given as under: If CH 4 is R1 and C 2 H 4 is R8 and C 2 H 2 is R5 and EWC 2 H 4 is R1 and EWC 2 H 2 is R4, then fault is D1 and severity is medium If CH 4 is R1 and C 2 H 4 is R2 and C 2 H 2 is R5 and EWC 2 H 4 is R3 and EWC 2 H 2 is R3, then fault is D2 and severity is high If CH 4 is R1 and C 2 H 4 is R3 and C 2 H 2 is R1 and EWC 2 H 4 is R3 and EWC 2 H 2 is R1 then fault is T2 and severity is low If CH 4 is R1 and C 2 H 4 is R4 and C 2 H 2 is R1 and EWC 2 H 4 is R5 and EWC 2 H 2 is R1, then fault is T3 and severity is high Defuzzification: Defuzzification provides the crisp value of output.…”

Section: Severity Of Duval Triangle Fault Regionsmentioning

confidence: 99%

Diagnosis of incipient dominant and boundary faults using composite DGA method

Wani

Khan

Gupta

et al. 2017

Int Trans Electr Energ Syst

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Summary In this paper, an effort is being made to develop a fault diagnosis algorithm using Duval triangle method of dissolved gas analysis (DGA) as a backbone for more reliable fault diagnosis. The algorithm is based on integrated model which is the fusion of Duval triangle and IEC ratio method. It uses the conventional Duval triangle rules to detect dominant faults and IEC method rules to detect “no” and boundary faults. Hence, the proposed method simultaneously checks the normal operating conditions, boundary and dominant fault cases. A graphical‐user interface (GUI) has also been prepared using LabVIEW. Study of fault severity is also carried out for various Duval triangle zones. Fault severity is determined using concept of energy weighing, and fuzzy model has been developed for the same. The system has been tested using a fault database and shows accuracy of 93.6%. Results presented indicate a trend towards a more reliable system.

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Performance Enhancement and Extension of DGA-Based Transformer Fault Diagnosis Methods Using Soft-Computing Techniques

Khan

Wani

et al. 2020

Advances in Intelligent Systems and Computing

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Fault severity determination in transformers using dissolved gas analysis(DGA)

Cited by 19 publications

References 13 publications

Transformer incipient fault diagnosis on the basis of energy-weighted DGA using an artificial neural network

Transformer incipient fault diagnosis on the basis of energy-weighted DGA using an artificial neural network

Diagnosis of incipient dominant and boundary faults using composite DGA method

Performance Enhancement and Extension of DGA-Based Transformer Fault Diagnosis Methods Using Soft-Computing Techniques

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