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

Khan, Shakeb A.; Khan, M. Ajmal; Wani, Shufali Ashraf; Equbal, Md. Danish

doi:10.1007/978-981-15-1532-3_13

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…In addition, for transformer incipient fault diagnosis, the Duval's triangle method mandates a fault condition to exist as it does not have the provision to diagnose a non-fault condition. It assigns a fault type to the no-fault cases, thus giving a wrong diagnosis (Duval, 2003) (Khan et al, 2020) (Singh and Bandyopadhyay, 2010b…”

Section: Duval's Triangle Methods Of Incipient Fault Identificationmentioning

confidence: 99%

“…Hence, the fault interpretation standards have apparent shortcomings in the form of no diagnosis or unresolved diagnosis. This calls for use of other tools that would aid the standards in the diagnosis process by removing these apparent fallacies (Duval, 2003;Duval, 2002;Singh and Bandyopadhyay, 2010a;Wani et al, 2015;Khan et al, 2020;Benmahamed et al, 2021;Zhang et al, 2022). E-Nose system is essentially an array of sensors which can respond to a wide range of inputs.…”

Section: Introductionmentioning

confidence: 99%

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IOT based classification of transformer faults using emerging techniques of E-nose and ANFIS

Equbal

Nezami²,

Hashem³

et al. 2022

Front. Energy Res.

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E-Nose finds its use in a wide range of applications such as quality assessment in food processing to toxic gas identification in chemical industry either in the offline or online mode. Their usage can be extended to transformer condition monitoring in the online mode. Considering the importance of transformers in power system and the impact it could create if faults in them are unidentified or left unattended, their functioning should be monitored on a real time basis. This work, describes the realization of a prospective E-Nose for online transformer incipient fault identification. The resistive gas sensor array has been simulated in real time using variable resistances forming one arm of a Wheatstone bridges. Separate variable resistances have been calibrated using characteristics of different fault gas sensors. The sensor array of the E-Nose helps to identify the transformer fault gases resulting from an incipient fault condition at the nascent stage itself and prompts for the necessary corrective action well before a catastrophic situation arises. Furthermore, ANFIS model of the Duval’s Triangle (DT) method have been developed to facilitate the online classification of incipient faults. The ANFIS models of other popularly used incipient fault interpretation methods, reported in earlier works, have also been used for a comparative analysis on their diagnostic capabilities. The developed model has been tested using the fault cases of IEC-TC10 fault database and the results thus obtained have been found to be very promising.

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Section: Duval's Triangle Methods Of Incipient Fault Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

IOT based classification of transformer faults using emerging techniques of E-nose and ANFIS

Equbal

Nezami²,

Hashem³

et al. 2022

Front. Energy Res.

Self Cite

View full text Add to dashboard Cite

show abstract

Transformer inter-turn winding fault detection based on no-load active power loss and reactive power

Ehsanifar

Allahbakhshi

Tajdinian

et al. 2021

International Journal of Electrical Power & Energy Systems

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Support for Decision-Making to Ensure Reliable Operation of Transformers as Part of a Responsible Power Facility

Levin

Yahya

2020

2020 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon)

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

Cited by 3 publications

References 28 publications

IOT based classification of transformer faults using emerging techniques of E-nose and ANFIS

IOT based classification of transformer faults using emerging techniques of E-nose and ANFIS

Transformer inter-turn winding fault detection based on no-load active power loss and reactive power

Support for Decision-Making to Ensure Reliable Operation of Transformers as Part of a Responsible Power Facility

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