Detection of Transformer Faults Using Frequency-Response Traces in the Low-Frequency Bandwidth

Portilla, Wilder Herrera; Mayor, Guillermo Aponte; Guerra, Jorge Pleite; González-García, Carlos

doi:10.1109/tie.2013.2282605

Cited by 31 publications

(7 citation statements)

References 20 publications

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“…Furthermore, the simulation and experimental test have been performed without considering a magnetic core owing to the fact that within a high-frequency range (usually higher than 10 KHz), inductance changes due to increased eddy current in the core. Indeed, the magnetic field originated from eddy current prevents the penetration of magnetic flux into the core [31]. Therefore, within a high-frequency range, the magnetic core can be overlooked.…”

Section: Table 2 the Capacitance Between The Hv And LV Disks (Pf)mentioning

confidence: 99%

Effect of the Asymmetrical Axial Displacement of Transformer Windings on Fra Characteristics

Ansari¹,

Vahedi²

2020

jcr

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Frequency response analysis (FRA) is one of the most reliable and useful methods for mechanical fault detection in transformers. The winding axial displacement is one of the mechanical fault types that can occur in transformers. Axial displacements causes the detailed model parameters changing, in response to which FRA characteristics will also change. Numerous researchers have considered winding axial displacements as a symmetrical movement of disks. However, one of the most prevalent types of winding axial displacements is asymmetrical axial displacement which has not been considered by any prior studies. In this paper, physical asymmetrical and symmetrical axial displacements of the first disk of the LV winding were simulated and FRA characteristics were obtained for these cases. Finally, experimental tests were performed on an experimental transformer to validate the simulation results.

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Section: Table 2 the Capacitance Between The Hv And LV Disks (Pf)mentioning

confidence: 99%

Effect of the Asymmetrical Axial Displacement of Transformer Windings on Fra Characteristics

Ansari¹,

Vahedi²

2020

jcr

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“…After the internal short circuit happens, electromagnetic force would cause winding deformation and frequency response analysis (FRA) is very sensitive to this [6]. Accuracy and application of FRA method have been extensively discussed in [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Method of inter‐turn fault detection for next‐generation smart transformers based on deep learning algorithm

Duan

Zhao

et al. 2019

High Voltage

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In this study, an inter-turn fault diagnosis method is proposed based on deep learning algorithm. 12-channel data is obtained in MATLAB/Simulink as the time-domain monitoring signals and labelled with 16 different fault tags, including both primary and secondary voltage and current waveforms. An auto-encoder is presented to classify the fault type of the abundant and comprehensive fault waveforms. The overall waveforms compose a two-dimension data matrix and the auto-encoder is trained to extract the features in the multi-channel waveforms. The selected features are convoluted with the original data, generating a one-dimensional vector as the input to the softmax classifier. Variables such as type, activation function and depth of auto-encoder, sparsity of sparse auto-encoder, number of features and pooling strategies are studied, which gives an intuitive process to train a proper learning model. The overall recognition accuracy reaches 99.5%. Signal characteristics such as channel selection, time span of the input signal and signal sampling frequency are studied to find the best solution for the interturn fault detection of the three-phase transformer. The proposed method under deep learning framework increases the accuracy and robustness in transformer fault diagnosis, indicating its potential and prospect in the next-generation smart transformers.

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“…Alternatively, there are proposals suggesting the use of models to perform the diagnosis [10][11][12][13]. However, experience has shown that the different frequency bandwidths of the SFRA trace are related to different elements within the transformer, in the lowfrequency bandwidth greatly influences the magnetic core [14].…”

Section: Introductionmentioning

confidence: 99%

Detection of Transformer Faults Using Frequency-Response Traces in the Low-Frequency Bandwidth

Cited by 31 publications

References 20 publications

Effect of the Asymmetrical Axial Displacement of Transformer Windings on Fra Characteristics

Effect of the Asymmetrical Axial Displacement of Transformer Windings on Fra Characteristics

Method of inter‐turn fault detection for next‐generation smart transformers based on deep learning algorithm

Analysis of the non‐linear behaviour of the magnetisation inductance during the frequency response test of a transformer

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