A new technique for separation of partial discharge sources and electromagnetic noise in radiofrequency measurements using energy ratios of different antennas

Ardila-Rey, Jorge Alfredo; Cerda-Luna, Matias Patricio; Rozas-Valderrama, Rodrigo; Castro, Bruno Albuquerque de; Andreoli, André Luiz; Cevallos, Benjamín

doi:10.1049/hve2.12065

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…A similar work was reported in [38]. Besides, other feature extraction methods based on time-frequency transformation can also be found in [39], [40], [41], [42], and [43], interested readers can refer to the original articles for more details.…”

Section: ) Transform Domain-based Featuresmentioning

confidence: 76%

A Comprehensive Review of Signal Processing and Machine Learning Technologies for UHF PD Detection and Diagnosis (II): Pattern Recognition Approaches

Long,

Xie,

Wang

et al. 2024

IEEE Access

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Partial discharge (PD) pattern recognition approaches are designed to identify the types or severities of the insulation defects within the high voltage equipment, which is vital for evaluating potential harmfulness and making follow-up maintenance plan. In recent years, many advanced machine learning (ML) algorithms have been introduced to this field and achieved remarkable results. As the second one of the two-part papers, we aim to give a comprehensive review regarding the pattern recognition approaches for ultra-high frequency (UHF) PD data in this paper. These methods are grouped into three categories, which are the traditional ML-based PD type recognition, the deep learning-based (DL) PD type recognition, and PD severity assessment. Specifically, for the first topic, feature extraction methods, dimensionality reduction methods and classification methods are reviewed separately. For the second topic, many state-of-the-art DL methods are discussed, including the deep belief network (DBN), deep autoencoder network (DAN), convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), graph convolutional network (GCN), deep ensemble learning (DEL), etc. For the third topic, the relevant algorithms are also divided into the conventional ML-based ones and the DL-based ones, which are studied in detail respectively. Finally, a brief discussion about the application effects of the above technologies is given, and some future directions are suggested. This paper covers almost every aspect of the PD pattern recognition and highlights the latest progress, which can provide valuable references for scholars in this field.

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Section: ) Transform Domain-based Featuresmentioning

confidence: 76%

A Comprehensive Review of Signal Processing and Machine Learning Technologies for UHF PD Detection and Diagnosis (II): Pattern Recognition Approaches

Long,

Xie,

Wang

et al. 2024

IEEE Access

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“…The proposed algorithm, called the chromatic technique, proved to be effective in differentiating corona-type discharges and the discharges in bushings and Nomex paper [19]. Another work proposed by Ardila-Rey et al (2021) differentiated surface discharges in Nomex paper and internal discharges in methacrylate-induced voids based on UHF tests [15].…”

Section: Partial Discharges In Dry-type Transformersmentioning

confidence: 99%

“…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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“…Oil-immersed power equipment (such as transformers, reactors etc.) is key equipment for power system transmission and distribution, which can improve the power quality of the power system, and its good operating condition is of great significance for the safe, economic and reliable operation of the power grid [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

The bubble formation characteristics of oil‐paper insulation and its influence on insulation performance

Zhang

Wei

et al. 2023

High Voltage

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Engineering experience shows that for transformers with long service life and serious moisture exposure, the inter‐turn paper insulation inside the winding will precipitate bubbles under the condition of electric field and conductor heating, which will endanger the equipment insulation. In order to explore the influence factors of bubbles formation in the moist insulating paper of transformers, based on the existing research on the influence of moisture content and gas dissolution in transformer oil, this study further considers the influence of air pressure (AP) and electric field and establishes an experimental platform to change the air pressure and electric field, thereby studying the formation characteristics of bubbles in oil‐paper insulation. The results show that air pressure and electric field can affect the initial temperature, volume, and shape of bubble formation. From 0 to −0.05 MPa for air pressure, the initial temperature of bubble generation decreased by 41°C, its maximum two‐dimensional projected area increased by 418.4% with more severe distortion, and the breakdown voltage decreased. When the AC voltage applied on the needle plate electrode increased from 0 to 10 kV, the initial temperature of bubble formation increased by 29°C, and the maximum two‐dimensional projection area increased by 336.6%.

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A new technique for separation of partial discharge sources and electromagnetic noise in radiofrequency measurements using energy ratios of different antennas

Cited by 7 publications

References 12 publications

A Comprehensive Review of Signal Processing and Machine Learning Technologies for UHF PD Detection and Diagnosis (II): Pattern Recognition Approaches

A Comprehensive Review of Signal Processing and Machine Learning Technologies for UHF PD Detection and Diagnosis (II): Pattern Recognition Approaches

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

The bubble formation characteristics of oil‐paper insulation and its influence on insulation performance

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