A new fault diagnostic technique in oil-filled electrical equipment; the dual of Duval triangle

Irungu, G. K.; Akumu, A. O.; Munda, Josiah L.

doi:10.1109/tdei.2016.005927

Cited by 32 publications

(13 citation statements)

References 20 publications

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“…Input type % Accuracy IEC method [30] Gas ratios 50.26 Refined IEC method [30] 66.06 IEC-based ANN [31] Gas ratios 80 ANN [32] Gas concentrations 89 IEC-599 [10] Gas ratios 77.78 Dual of Duval triangle [10] Relative gas concentrations 90.6 EWDGA-based ANN model Gas concentrations 86%…”

Section: Methodsmentioning

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: Methodsmentioning

confidence: 99%

“…Various elaborate algorithms have been suggested to improve the diagnosis of the available ratio-based methods for a more reliable diagnosis, as in [2][3][7][8][9][10]. Furthermore, several soft computing techniques have also been used in various studies to overcome the shortcomings of the different standard methods.…”

Section: Introductionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…The strongest firing rule will be: Using FL can be tedious, and errors can creep in during rules formulation; instead, the fault identification can be completed from the fuzzified inputs degree of memberships using evidential reasoning (ER). ER is firm mathematical theory started by Dempster and advanced by Shaffer [29][30][31][32][33][34]. It has superior ability to combine evidence from various data sources to actionable information.…”

Section: Fault Identification Based On Artificial Intelligence Techniquesmentioning

confidence: 99%

Application of Dissolved Gas Analysis in Assessing Degree of Healthiness or Faultiness with Fault Identification in Oil-Immersed Equipment

Irungu

Akumu

2020

Energies

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The healthiness and or faultiness of oil-immersed electrical equipment using dissolved gas characterization has remained a critical and challenging task in power systems. Dissolved gas analysis (DGA) continues to be the utmost preferred technique of detecting mainly slow evolving thermal and electrical faults. However, DGA can reveal more than just faults in equipment. This research looks at broad areas where DGA can be applied to determine the healthiness or faultiness of equipment in addition to fault identification. In equipment considered normal—i.e., fault-free—DGA can give the degree of healthiness (DOH) based on Rogers ratios C2H2/C2H4 < 0.1, 0.1 < CH4/H2 < 1, and C2H4/C2H6 < 1, plus the 3 < CO2/CO < 10 ratio for identifying fault-free devices. This answers the question: How healthy or normal is the equipment? Similarly, when these ratios are violated, it signifies the presence of faults, and two things ought to be determined. One is to identify the type of fault(s), which has been the norm. The other thing that can be evaluated is the degree of faultiness (DOF), based on the extent to which the ratios have been violated. Rarely has this been done. This might answer the question for the same fault class: How severe is the fault? To synthesize the DOH and/or DOF, fuzzy logic is applied. To diagnose faults, fuzzy logic and fuzzy-evidential tools are proposed. The accuracy and effectiveness of the proposed fuzzy techniques are better than those of the IEC60599 and Rogers methods, and they are comparable to those of the Duval Triangle 1 and Pentagon 1 methods using the six IEC faults. Results from DOF evaluation have shown electrical faults to be more impactful relative to the rest.

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“…Fault diagnosis models proposed by previous works adopted different types and sizes of input features, and sometimes critical features were not taken into account. For example, DTM is regarded as the most accurate approach among conventional methods [19], which indicates that input features of DTM may reveal potential information of DGA samples better than that of other methods, but most of the mentioned works have ignored those features. Besides, some potentially useful features proposed recently are not taken into consideration.…”

Section: Introductionmentioning

confidence: 99%

Hybrid feature selection approach for power transformer fault diagnosis based on support vector machine and genetic algorithm

Kari

Gao

Zhao

et al. 2018

IET Generation, Transmission & Distribution

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A new fault diagnostic technique in oil-filled electrical equipment; the dual of Duval triangle

Cited by 32 publications

References 20 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

Application of Dissolved Gas Analysis in Assessing Degree of Healthiness or Faultiness with Fault Identification in Oil-Immersed Equipment

Hybrid feature selection approach for power transformer fault diagnosis based on support vector machine and genetic algorithm

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