Maintenance diagnostics of transformers considering the influence of short-circuit currents during operation

Gutten, Miroslav; Korenčiak, Daniel; Kučera, Matej; Šebök, Milan; Opielak, Marek; Kołtunowicz, Tomasz N.

doi:10.17531/ein.2017.3.17

Cited by 27 publications

(9 citation statements)

References 3 publications

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“…Special focus is made on comparison of the currently available dielectric spectroscopy methods with traditional measurement techniques like dielectric dissipation factor (tgδ) tests at power frequency and 0.1 Hz, dielectric adsorption ratio and the polarization index [18]. Applying a sinus frequency sweep in the range from 0.0005 Hz to 10 kHz is used to measure the dielectric responses depending on the quality of transformer insulation [19].…”

Section: Diagnostic Insulating Analysis Of Transformersmentioning

confidence: 99%

Frequency Diagnostics of Insulating System of Power Transformers

Gutten

Korenčiak

Brncal

et al. 2020

Electrical, Control and Communication Engineering

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The paper presents experimental diagnostics of the oil-paper insulating system of power transformers using progressive frequency method. The first part of the paper deals with the basic information about diagnostics of power oil transformers. The second part of the paper deals with the use of frequency domain spectroscopy (FDS) for power oil transformers. This method is used in the analysis of insulating condition of power transformer with the system of oil-paper. It was found that the results of these tests are highly influenced by the operating temperature during the experimental measurement. Moisture and conductivity between the insulating transformer paper and oil in an insulating system are highly dependent on the temperature. In the other part, the paper presents experimental results of the diagnostic measurement for a real traction oil transformer at different operating temperatures and states (with oil and without). Finally, the paper presents comparative measurement among several autotransformers with the same power. In experimental measurements, the correlation between the measured values and the operational aspects of the individual oil autotransformers are shown.

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Section: Diagnostic Insulating Analysis Of Transformersmentioning

confidence: 99%

Frequency Diagnostics of Insulating System of Power Transformers

Gutten

Korenčiak

Brncal

et al. 2020

Electrical, Control and Communication Engineering

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“…The RVM method consists of plotting the measured maximum response times with respect to the charging time, from which it is possible to determine the moisture content of the insulation in highvoltage oil equipment. In general, this method is intended for non-destructive, off-line determination of the state of the isolation system of transformers, cables or other devices that are comprised of the conductor and the insulator [11].…”

Section: Diagnostics Of Insulating Transformer Properties By Time Metmentioning

confidence: 99%

Diagnostics of insulating transformer properties

Gutten¹,

Korenčiak²,

Brandt³

2019

IJSGCE

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Article presents experimental analyses of transformers by insulating progressive time method RVM (Return Voltage measurement). The first part of article deals with the possibilities of diagnostic insulating analysis of power transformers. In the conclusion are the results which have mainly shows moisture content in distribution transformer 22/0.4kV. Thanks transformer diagnostics is assured higher protection of the environment from possible accidents of certain transformer.

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“…To analyze the characteristics of different PD types, PD signals of different models are extracted in the laboratory. According to the inner insulation structure of power transformers [28,29], there are four possible different PD types, including floating discharge (FD), needle-plate discharge (ND), surface discharge (SD), and air-gap discharge (AD). PD models are shown in Figure 2.…”

Section: Signal Extractionmentioning

confidence: 99%

Partial Discharge Feature Extraction Based on Ensemble Empirical Mode Decomposition and Sample Entropy

Shang

2017

Entropy

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Partial Discharge (PD) pattern recognition plays an important part in electrical equipment fault diagnosis and maintenance. Feature extraction could greatly affect recognition results. Traditional PD feature extraction methods suffer from high-dimension calculation and signal attenuation. In this study, a novel feature extraction method based on Ensemble Empirical Mode Decomposition (EEMD) and Sample Entropy (SamEn) is proposed. In order to reduce the influence of noise, a wavelet method is applied to PD de-noising. Noise Rejection Ratio (NRR) and Mean Square Error (MSE) are adopted as the de-noising indexes. With EEMD, the de-noised signal is decomposed into a finite number of Intrinsic Mode Functions (IMFs). The IMFs, which contain the dominant information of PD, are selected using a correlation coefficient method. From that, the SamEn of selected IMFs are extracted as PD features. Finally, a Relevance Vector Machine (RVM) is utilized for pattern recognition using the features extracted. Experimental results demonstrate that the proposed method combines excellent properties of both EEMD and SamEn. The recognition results are encouraging with satisfactory accuracy.

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Maintenance diagnostics of transformers considering the influence of short-circuit currents during operation

Cited by 27 publications

References 3 publications

Frequency Diagnostics of Insulating System of Power Transformers

Frequency Diagnostics of Insulating System of Power Transformers

Diagnostics of insulating transformer properties

Partial Discharge Feature Extraction Based on Ensemble Empirical Mode Decomposition and Sample Entropy

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