Separation Techniques of Partial Discharges and Electrical Noise Sources: A Review of Recent Progress

Ardila-Rey, Jorge Alfredo; Cerda-Luna, Matias Patricio; Rozas-Valderrama, Rodrigo; Castro, Bruno Albuquerque de; Andreoli, André Luiz; Muhammad‐Sukki, Firdaus

doi:10.1109/access.2020.3035249

Cited by 26 publications

(9 citation statements)

References 60 publications

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“…Wavelet transform approach to adaptive extraction of PD pulses from interferences was applied. The article [34] describes the main clustering techniques that have been used over time to separate PD sources and electrical noise.…”

Section: Original Papermentioning

confidence: 99%

Extraction of Partial Discharge Signal in Predominantly VHF Frequency Range in the Presence of Strong Noise in Power Transformer

Dukanac¹

2022

Preprint

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This paper provides a detailed analysis of the online detected partial discharge (PD) in the predominantly VHF range in the power transformer during normal operation in the thermal power plant. A standard UHF drain valve sensor is used with the ability to also capture VHF frequencies of received signals. If PD detection is performed during off-line testing, there are excessive costs proportional to the time during which the power transformers are disconnected from the network. For this economic reason, online PD detection techniques are more convenient. The UHF technique has a higher signal-to-noise ratio compared to IEC 60270 and the acoustic method. To accurately determine the strength and waveform of the PD signal, especially if the source position is far from the UHF sensor or if the signal is weak, it is necessary to properly separate the useful part of the recorded signal from the background noise. The criterion for this is that there are no time shifts of the first peaks of the most prominent PD. For that reason, the beginning and the approximate end of PD signal has to be determined. The results show some obvious similarities of PDs in the recorded signals, such as frequency range, duration, repetition rate and the same dominant frequency, which sufficiently indicates that it is the same type of PD.

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Section: Original Papermentioning

confidence: 99%

Extraction of Partial Discharge Signal in Predominantly VHF Frequency Range in the Presence of Strong Noise in Power Transformer

Dukanac¹

2022

Preprint

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“…One of the main challenges of using PRPD for the classification process, and subsequent diagnosis, is the simultaneous presence of multiple sources of PD or noise. The patterns obtained under these conditions are difficult to interpret, because the signals can overlap, and higher-amplitude sources can hide the presence of other types of sources with lower amplitudes [29,32,33]. For this reason, it is recommended to perform a separation process to assist in defect identification [33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Identification of Partial Discharge Sources by Feature Extraction from a Signal Conditioning System

Carvalho,

da Costa,

Nobrega

et al. 2024

Sensors

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This paper addresses the critical challenge of detecting, separating, and classifying partial discharges in substations. It proposes two solutions: the first involves developing a signal conditioning system to reduce the sampling requirements for PD detection and increase the signal-to-noise ratio. The second approach uses machine learning techniques to separate and classify PD based on features extracted from the conditioned signal. Three clustering algorithms (K-means, Gaussian Mixture Model (GMM), and Mean-shift) and the Support Vector Machine (SVM) method were used for signal separation and classification. The proposed system effectively reduced high-frequency components up to 50 MHz, improved the signal-to-noise ratio, and effectively separated different sources of partial discharges without losing relevant information. An accuracy of up to 93% was achieved in classifying the partial discharge sources. The successful implementation of the signal conditioning system and the machine learning-based signal separation approach opens avenues for more economical, scalable, and reliable PD monitoring systems.

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“…Therefore, the development of a partial discharge monitoring tool can prevent the transformers' total failures. Among several methodologies [13][14][15][16][17], two promising techniques are related to current analysis and the acoustic emission technique. In the first, the behavior of the electric current is analyzed to identify the transients generated by PD activity [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis Applied to the Partial Discharges Identification in Dry-Type Transformers by Hall and Acoustic Emission Sensors

Castro

Santos

Lucas

et al. 2022

Sensors

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Dry-type insulated transformers stand out for their higher applicability in substations, high-voltage instrumentation systems, and electrical installations. In this machine, the insulation system is constituted of dielectric materials such as epoxy resin and Nomex paper. Some critical issues in the operation of this equipment, such as overload, moisture, or heat, can induce a slow degradation of the physical–chemical properties of the dielectric materials, which can culminate in the total failure of the transformer. However, before the transformer’s shutdown, it is common to detect discharge activity in the insulation system. Based on this issue, this work proposes an experimental and comparative analysis between acoustic emission and Hall-effect sensors, aiming at differentiating discharges in epoxy resin and Nomex paper, materials that constitute the insulation of the dry-type insulated transformers. Two signal processing techniques were studied: traditional frequency analysis and discrete wavelet transform. The objective is to develop signal processing techniques to differentiate each type of discharge since different discharges require different maintenance actions. The results obtained indicate that acoustic emission sensors and Hall sensors are promising in differentiating discharge in epoxy resin and Nomex paper. Furthermore, the pattern recognition tools presented by this work, which associated the wavelet levels energies and the energy of the full signals with the average band and the equivalent bandwidth, were effective to perform feature extraction of power transformer condition.

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Separation Techniques of Partial Discharges and Electrical Noise Sources: A Review of Recent Progress

Cited by 26 publications

References 60 publications

Extraction of Partial Discharge Signal in Predominantly VHF Frequency Range in the Presence of Strong Noise in Power Transformer

Extraction of Partial Discharge Signal in Predominantly VHF Frequency Range in the Presence of Strong Noise in Power Transformer

Identification of Partial Discharge Sources by Feature Extraction from a Signal Conditioning System

A Comparative Analysis Applied to the Partial Discharges Identification in Dry-Type Transformers by Hall and Acoustic Emission Sensors

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