A Novel Online Technique to Detect Power Transformer Winding Faults

Abu-Siada, A.; Islam, Syed

doi:10.1109/tpwrd.2011.2180932

Cited by 174 publications

(87 citation statements)

References 19 publications

(18 reference statements)

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“…According to [20][21][22], radial deformation is simulated by randomly changing the value of the capacitance HV and of LV windings while the change in inductance is neglected. The study did not emphasize the amount of change in the capacitance that is corresponding to the studied fault levels.…”

Section: Impact Of Buckling Deformation On Equivalent Electric Circuimentioning

confidence: 99%

Improved power transformer winding fault detection using FRA diagnostics – part 2: radial deformation simulation

Hashemnia

Abu-Siada

Islam

2015

IEEE Trans. Dielect. Electr. Insul.

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Frequency response analysis (FRA) is proven to be a powerful tool to detect winding deformation within power transformers. Although the FRA test along with the equipment are well developed, interpretation of FRA signature is still a challenge and it needs skilled personnel to identify and quantify the fault type if exists as at this stage, there is no reliable standard code for FRA signature classification and quantification. As it is very hard to implement faults on physical transformer without damaging it, researchers investigated the impact of various mechanical winding deformations on the transformer FRA signature by randomly changing the value of particular electrical parameters of the transformer equivalent electrical circuit. None of them however, precisely investigated the correlation between physical fault level and the percentage change in each parameter. In this paper, the physical geometrical dimension of a singlephase transformer is simulated using 3D finite element analysis to emulate the real transformer operation. A physical radial deformation of different fault levels is simulated on both low voltage and high voltage windings. The impact of each fault level on the electrical parameters of the equivalent circuit is investigated and the correlation between the fault level and the percentage change in each parameter of the equivalent circuit is provided. This will facilitate precise fault simulation using transformer equivalent electrical circuit and ease the quantification analysis of FRA signature.

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Section: Impact Of Buckling Deformation On Equivalent Electric Circuimentioning

confidence: 99%

Improved power transformer winding fault detection using FRA diagnostics – part 2: radial deformation simulation

Hashemnia

Abu-Siada

Islam

2015

IEEE Trans. Dielect. Electr. Insul.

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“…The vector potential is chosen so that = × which fulfiles . × (1) The average complex flux can be obtained by integrating the vector potential across the conductor's cross-section as shown in (2). The reflected inductances and losses attributed to the windings can be used and represented in the model as linear lumped elements.…”

Section: Finite Element Analysismentioning

confidence: 99%

“…If these forces exceed the withstand capability of the transformer, winding deformation may occur. One of the causes of transformer mechanical damages and windings deformation is the loss of clamping pressure because of insulation degradation resulted by ageing [1,2]. While with only minor winding deformation transformer can still working normally, its capability to withstand any further through faults will gradually decrease.…”

mentioning

confidence: 99%

Impact of axial displacement on power transformer FRA signature

Hashemnia

Abu-Siada

Islam

2013

2013 IEEE Power &Amp; Energy Society General Meeting

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Abstract-Frequency response analysis (FRA) is gaining global popularity in detecting power transformer winding movement and core deformation due to the development of FRA test equipment. However, because FRA relies on graphical analysis, interpretation of its signatures is still a very specialized area that calls for skillful personnel to detect the sort and likely place of the fault as so far, there is no reliable standard code for FRA signature classification and quantification. This paper investigates the impact of transformer winding axial displacement on its FRA signature as a step toward the establishment of reliable codes for FRA interpretation. In this context a detailed model for a single-phase transformer is simulated using 3D finite element analysis to emulate a close to real transformer. The impact of axial displacement on the electrical distributed parameters model that are calculated based on the transformer physical dimension is examined to investigate how model's parameters including inductance and capacitance matrices change when axial displacement takes place within a power transformer.

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“…However, the installation time of most power transformers worldwide dates back to the 1980s and they are reaching their deadline of designed life cycle [2,3]. It is also reported in many studies that the failure rate of power transformers has recently increased globally.…”

Section: Introductionmentioning

confidence: 99%

Identification of Power Transformer Winding Mechanical Fault Types Based on Online IFRA by Support Vector Machine

et al. 2017

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Abstract:A transformer is the most valuable and expensive property for power utility, thus ensuring its reliable operation is a major task for both operators and researchers. Online impulse frequency response analysis has proven to be a promising technique for detecting transformer internal winding mechanical deformation faults when a power transformer is in service. However, as so far, there is still no reliable standard code for frequency response signature interpretation and quantification. This paper tries to utilize a machine learning method, namely the support vector machine, to identify and classify the winding mechanical fault types, based on online impulse frequency response analysis. Actual transformer fault data from a specially manufactured model transformer are collected and analyzed. Two feature vectors are proposed and the diagnostic results are predicted. The diagnostic results indicate the satisfied classifying accuracy by the proposed method.

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A Novel Online Technique to Detect Power Transformer Winding Faults

Cited by 174 publications

References 19 publications

Improved power transformer winding fault detection using FRA diagnostics – part 2: radial deformation simulation

Improved power transformer winding fault detection using FRA diagnostics – part 2: radial deformation simulation

Impact of axial displacement on power transformer FRA signature

Identification of Power Transformer Winding Mechanical Fault Types Based on Online IFRA by Support Vector Machine

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