Impulse testing of power transformers using the transfer function method

Malewski, R.; Poulin, Bertrand

doi:10.1109/61.4283

Cited by 139 publications

(34 citation statements)

References 10 publications

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“…Oxidative and aging behavior of transformer oil. The protection against the oxidation and aging is a factor related only to the amount and the type of aromatic compound present in transformer and available in oxygen [4,5].No difference was found in the behavior of oil that could be related to their molecular composition.…”

Section: Structural Analysis Of Oilmentioning

confidence: 99%

Critical Investigation of Heat Transfer in Oil Cooled Transformer

2017

IJSR

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Power transformer represents the largest portion of capital investment in transmission and distribution substations.Transformer functional life is influenced by thermal, mechanical and dielectricstresses. The concept of insulation 'Thermal life 'is a useful one, in which it can set a standard level of physical condition below which insulation integrity may be prejudiced by mechanical or dielectric stresses, normally experienced in service. In power transformer, the oil serves a dual purpose as insulating and cooling medium. The heat generated in the transformer is removed by its oil surrounding the source and is transmitted either to atmospheric air or water. Thermal modeling of power transformer top-oil considering viscosity and conductivity and other relevant parameters have been taken into account in the present work and accordingly an important correlation has been derived for ONAF (oil natural air forced) type of cooling. Result shows that the transformer top oil temperature varies remarkably with respect to time.However,within the range of the data covered the plotted figures show that rapid increase in the temperature of the transformer top oil could be achieved with increasing values of time after a certain period of time.

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Section: Structural Analysis Of Oilmentioning

confidence: 99%

Critical Investigation of Heat Transfer in Oil Cooled Transformer

2017

IJSR

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“…For many years, the applied voltage waveforms and the resulting current waveforms were analyzed manually by studying oscillographic records [2], which were strongly dependent on the knowledge and experience of the experts performing the analysis. With the advent of digital recorders and analyzers, frequency-domain-based analysis became popular, particularly the transfer function approach [3] for fault classification. In recent years, the use of computeraided techniques like expert systems [4], and artificial neural network (ANNs) [5] have been employed for impulse fault classification in transformers.…”

Section: Introductionmentioning

confidence: 99%

Wavelet-Aided SVM Tool for Impulse Fault Identification in Transformers

Koley

Purkait

Chakravorti

2006

IEEE Trans. Power Delivery

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Accurate diagnosis of faults in transformers can significantly enhance the safety, reliability, and economics of power systems. In the case of a fault, it has been established that the pattern of the fault currents contain a typical signature of the nature and location of the fault for a given winding. This paper describes a new approach using wavelet transform (WT) for extraction of features from the impulse test response of a transformer in time-frequency domain and support vector machine in regression mode to classify the patterns inherent in the features extracted through the WT of different fault currents. This paper also describes an approach to identify the type and location of the transformer faults accurately by analyzing experimental impulse responses that contain noise. Here, experimental impulse responses have been preprocessed with the help of wavelet-packet filters to remove the unwanted noise from the signal and thereby enhance the analyzing capability of continuous wavelet transform.Index Terms-Analog model, digital model, impulse test, support vector machine (SVM), time-frequency analysis, transformer insulation, wavelet packet (WP) transform, wavelet transform (WT).

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“…But this technique was strongly dependent on the knowledge and experience of the experts performing the analysis. With the advent of digital recorders and analyzers, frequency domain based analysis became popular, particularly the transfer function approach [3], for fault classification. In recent years, the use of computer aided techniques like Expert Systems [4], and Artificial Neural Network s(ANN) [5] have been employed for impulse fault classification in transformers.…”

Section: Introductionmentioning

confidence: 99%