Experience with frequency response analysis (FRA) for the mechanical condition assessment of transformer windings

Picher, P.; Rajotte, C.; Tardif, Carl

doi:10.1109/eic.2013.6554237

Cited by 13 publications

(11 citation statements)

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“…There are 4 test connections to perform the FRA measurement based on CIGRE WG A2/26 [ 19 ], IEEE Std C57.149–2012 [ 4 ] and IEC 60076–18 [ 20 ]. In this study, only end-to-end short circuit test was conducted for HV winding according to IEEE Std C57.149–2012 [ 4 ].…”

Section: Methodsmentioning

confidence: 99%

“…However, RMSE reduces the variance of error distribution. These methods were proposed by standards such as CIGRE WG A2.26 and IEEE Std C59.149–2012 [ 4 , 19 ]. In order to use the statistical indicators, the frequency range of the response needs to be divided into 3 frequency bands: low, medium and high.…”

Section: Methodsmentioning

confidence: 99%

“…The complexity of FRA/overvoltage application is comprehensible and quite detail in comparison with MTL and lumped circuits models. FEM model offers viable option to examine the impact of individual structural damages of the windings which can be costly to be carried out through experimental approach [ 19 ]. However, considering the time-consuming factor for FEM, there is a need to improve the approach and at the same time provides acceptable simulated frequency response.…”

Section: Introductionmentioning

confidence: 99%

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Extraction of winding parameters for 33/11 kV, 30 MVA transformer based on finite element method for frequency response modelling

et al. 2020

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This paper proposes an alternative approach to extract transformer's winding parameters of resistance (R), inductance (L), capacitance (C) and conductance (G) based on Finite Element Method (FEM). The capacitance and conductance were computed based on Fast Multiple Method (FMM) and Method of Moment (MoM) through quasi-electrostatics approach. The AC resistances and inductances were computed based on MoM through quasi-magnetostatics approach. Maxwell's equations were used to compute the DC resistances and inductances. Based on the FEM computed parameters, the frequency response of the winding was obtained through the Bode plot function. The simulated frequency response by FEM model was compared with the simulated frequency response based on the Multi-conductor Transmission Line (MTL) model and the measured frequency response of a 33/11 kV, 30 MVA transformer. The statistical indices such as Root Mean Square Error (RMSE) and Absolute Sum of Logarithmic Error (ASLE) were used to analyze the performance of the proposed FEM model. It is found that the simulated frequency response by FEM model is quite close to measured frequency response at low and mid frequency regions as compared to simulated frequency response by MTL model based on RMSE and ASLE analysis.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Extraction of winding parameters for 33/11 kV, 30 MVA transformer based on finite element method for frequency response modelling

et al. 2020

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“…Reference [22] examines the identification of winding type by SVM. Transformer insulation plays a major role in frequency response, with liquid insulation changing the permittivity of the material, increasing capacitances and, as a result, shifting resonances to lower frequencies [27]. Meanwhile the migration of moisture into solid insulation has been reported to shift resonances to higher frequencies [28].…”

Section: Frequency Response Analysismentioning

confidence: 99%

Frequency Response Analysis Interpretation Using Numerical Indices and Machine Learning: A Case Study Based on a Laboratory Model

et al. 2021

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Frequency response analysis is a powerful tool for mechanical fault diagnostics in power transformers. However, interpretation schemes still today depend on expert analyses, mainly because of the complex structure of power transformers. One of the fundamental shortcomings of experimental investigations is that mechanical deformations cannot be managed on real transformers to obtain data for different scenarios because they are too destructive. To address this issue in a systematic way, the current research used a specially designed laboratory transformer model that allows mechanical defects to be introduced so its frequency response can be evaluated under different conditions. The key feature of this model is the non-destructive interchangeability of its winding sections, allowing reproducibility and repeatability of frequency response measurements. Numerical indices were compared over key performance indicators (linearity, sensitivity and monotonicity). The analysis indicated that comparative standard deviation offered promising results for evaluation of mechanical deformations on the laboratory winding model given its monotonic behaviour, sensitivity and linear increase with fault severity. Additionally, support vector machine learning, radial basis function neural network and the statistical k-nearest neighbour method were used for fault classification with different strategies and configurations. While limited data from different transformers are used in the available literature, the approach discussed here considers 371 measurements from the same transformer model. The test results are supportive and demonstrate great accuracy when machine learning is used for winding fault classification.INDEX TERMS Condition monitoring, frequency response analysis interpretation, machine learning, numerical indices, power transformers, radial basis function, support vector machines.

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“…In a perfect case, this means that the results are compared to an earlier measurement taken in the same test configuration. In some cases, especially when the first measurement is performed on a given unit, the comparison is done between phases or on twin units, however, such results may be influenced by some additional effects [16].…”

Section: Frequency Response Analysismentioning

confidence: 99%

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

Kornatowski

Banaszak

2019

Energies

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Frequency response analysis (FRA) is a popular method for assessing a transformer’s mechanical condition. The paper proposes a new method for interpreting the frequency response measurement results. The currently used numerical indices only give one value, which may be misleading in the analysis, while the proposed frequency response quality index (FRQI) tool analyses three separate features in the whole frequency range. The applied numerical calculations technique allows for estimations of not only the values of the average quality indices, but also locally for given frequency ranges of the analysed spectrum. It allows for determination of the problems that can be found in the active part of a transformer. The presented results come from three transformers, representing cases of typical faults. Two of them are from industry, while one was used for deformational tests in laboratory conditions. The proposed FRQI method showed its usefulness in FRA test results analysis and may be introduced into the automated assessment of such data. Each of the component parameters is sensitive to other types of differences observed between the compared frequency response curves, and may be used as a good quality detection tool.

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Experience with frequency response analysis (FRA) for the mechanical condition assessment of transformer windings

Cited by 13 publications

References 1 publication

Extraction of winding parameters for 33/11 kV, 30 MVA transformer based on finite element method for frequency response modelling

Extraction of winding parameters for 33/11 kV, 30 MVA transformer based on finite element method for frequency response modelling

Frequency Response Analysis Interpretation Using Numerical Indices and Machine Learning: A Case Study Based on a Laboratory Model

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

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