Frequency Response Quality Index for Assessing the Mechanical Condition of Transformer Windings

Kornatowski, E.; Banaszak, S.

doi:10.3390/en13010029

Cited by 10 publications

(5 citation statements)

References 24 publications

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“…As a rule, in FDS alternating current tests, measurements are made at three points per decade [11,[50][51][52]. In this way, tests of power transformers have also been performed [53][54][55][56][57][58]. This is due to the very highly time-consuming characteristic of measurements at ultra-low frequencies.…”

Section: Methodsmentioning

confidence: 99%

Precise Measurements of the Temperature-Frequency Dependence of the Conductivity of Cellulose—Insulating Oil—Water Nanoparticles Composite

et al. 2020

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This article presents direct σDC and alternating σ(f) current conductivity measurements obtained by the frequency domain spectroscopy (FDS) method on cellulose-transformer oil–water nanoparticle composite with a moisture content of (5.0 ± 0.2)% by weight in a temperature range from 293.15 to 333.15 K with step of 8 K. The uncertainty of temperature maintenance during measurements was below ±0.01 K. The sample was prepared for testing in a manner as close as possible to the cellulose insulation moisturizing process in power transformers. For the analysis of the results obtained, a model of alternating and direct current hopping conductivity was used, based on the quantum phenomenon of electron tunneling between the potential wells and nanodrops of water. It was observed that on the d(logσ)/d(logf)-derived waveforms there was a clear low-frequency maximum, and a tendency to reach the next maximum in the high-frequency area was visible. On this basis it was established that the increase in conductivity takes place in two stages. It was found that the position of σ(f) waveforms in the double logarithmic coordinates is influenced by the temperature dependence both of the conductivity and of the relaxation time of the conductivity. These relationships are described with the appropriate activation energies of the conductivity and relaxation time of conductivity. Based on the analysis of experimental data using Arrhenius diagrams, average values of the activation energy of conductivity ΔWσ ≈ (0.894 ± 0.0134) eV and the relaxation time of conductivity ΔWτσ ≈ (0.869 ± 0.0107) eV were determined. The values were equal within the limits of uncertainty and their mean value was ΔW ≈ (0.881 ± 0.0140) eV. Using the mean value of the activation energy, the frequency dependence of conductivity, obtained at different temperatures, was shifted to 293.15 K. For this purpose, first the waveforms were shifted along the horizontal and then the vertical axis. It was found that after the shift the σ(f) waveforms for the different temperatures overlap perfectly. This means that the shape of the frequency dependence of the conductivity is determined by the moisture content of the pressboard. The position of the waveforms in relation to the coordinates is determined by the temperature relationships of the conductivity and the relaxation time of the conductivity.

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Section: Methodsmentioning

confidence: 99%

Precise Measurements of the Temperature-Frequency Dependence of the Conductivity of Cellulose—Insulating Oil—Water Nanoparticles Composite

et al. 2020

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“…In this study, we propose utilizing the quality indicator (QI), whose definition considers the similarity of shapes, average values, and dynamics of changes between two compared datasets, based on their probabilistic properties [17].…”

Section: Quality Index For Assessment Of Complementary Fra and Vm Res...mentioning

confidence: 99%

Quality Index for Assessment of the Mechanical Condition of Transformers’ Active Part with Frequency Response and Vibroacoustic Measurements

Kornatowski,

Banaszak,

Molenda

2024

Energies

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The paper describes the application of the numerical tool quality index for an objective evaluation of complementary frequency response analysis (FRA) and vibroacoustic method (VM) test methods. These diagnostic methods are used in the industrial practice of transformer diagnostics for the assessment of the mechanical condition of windings and a core. The quality index is based on a numerical comparison of the curve obtained from measurements and the reference curve in a frequency domain. The quality index is based on estimators for the covariance, variance, and expected values. First, both methods of analysis were applied to a group of transformers of similar construction, leading conclusions on quality index values being quickly drawn. Next, it was applied to another transformer’s FRA and VM measurement results. The results showed problems with its mechanical condition, thus confirming that the proposed methods can be used in the practical assessment of transformers with these two diagnostic methods. The assessment of transformer’s active-part mechanical condition with complementary FRA and VM methods can be performed much more easily with the proposed quality indices.

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“…FRA diagnostics using local assessment of quality indices is the actual scientific problem. The research in this field can be found in papers from top publishers [21,22] or from international conferences, for example [23]. An assessment based only on a single value returned by any numerical index may be misleading and result in a wrong interpretation of the measured FR data.…”

Section: Frequency Response Analysis and Assessment Of Measured Datamentioning

confidence: 99%

The Influence of the Window Width on FRA Assessment with Numerical Indices

2021

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Frequency response analysis is a method used in transformer diagnostics for the detection of mechanical faults or short-circuits in windings. The interpretation of test results is often performed with the application of numerical indices. However, usually these indices are used for the whole frequency range of the recorded data, returning a single number. Such an approach is inaccurate and may lead to mistakes in the interpretation. An alternative quality assessment is based on the estimation of the local values of the quality index with the moving window method. In this paper, the authors analyse the influence of the width of the input data window for four numerical indices. The analysis is based on the data measured on the transformer with deformations introduced into the winding and also for a 10 MVA transformer measured under industrial conditions. For the first unit the analysis is performed for various window widths and for various extents of the deformation, while in the case of the second the real differences between the frequency response curves are being analysed. On the basis of the results it was found that the choice of the data window width significantly influences the quality of the analysis results and the rules for elements number selection differ for various numerical indices.

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Frequency Response Quality Index for Assessing the Mechanical Condition of Transformer Windings

Cited by 10 publications

References 24 publications

Precise Measurements of the Temperature-Frequency Dependence of the Conductivity of Cellulose—Insulating Oil—Water Nanoparticles Composite

Precise Measurements of the Temperature-Frequency Dependence of the Conductivity of Cellulose—Insulating Oil—Water Nanoparticles Composite

Quality Index for Assessment of the Mechanical Condition of Transformers’ Active Part with Frequency Response and Vibroacoustic Measurements

The Influence of the Window Width on FRA Assessment with Numerical Indices

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