Classification and comparison of AC and DC partial discharges by pulse waveform analysis

Rostaghi-Chalaki, Mojtaba; Yousefpour, Kamran; Klüss, Joni; Kurum, Mehmet; Donohoe, J. Patrick; Park, Chanyeop

doi:10.1016/j.ijepes.2020.106518

Cited by 22 publications

(2 citation statements)

References 15 publications

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“…In [22], the authors extracted the waveform characteristics such as rise time, decay time, pulse width, amplitude from PD pulses to distinguish AC and DC PDs. They found that the most obvious differences between AC and DC PDs were reflected in the amplitude.…”

Section: ) Waveform-based Featuresmentioning

confidence: 99%

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: ) Waveform-based Featuresmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…Though subtle, the service life of insulation is significantly reduced as a result of erosion caused by PD, particularly when there are numerous and repetitive PDs under AC voltage stress. 47 As a result, PD is widely used for assessing the insulation quality of dielectric materials. 48 The PD behavior of LMPCs, as seen in Fig.…”

Section: Breakdown Electric Field and Partial Discharge Measurementmentioning

confidence: 99%

Homogeneity of liquid metal polymer composites: impact on mechanical, electrical, and sensing behavior

Hoang,

Faruqe,

Bury

et al. 2023

Soft Matter

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Non-Destructive Test Method for Diagnosing Turn-to-Turn Circuits of Electric Motor Windings Under Conditions of Local Reactive Power Compensation

Kryvonosov,

Matvienko,

Antipov

et al. 2024

Studies in Systems, Decision and Control

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Classification and comparison of AC and DC partial discharges by pulse waveform analysis

Cited by 22 publications

References 15 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

Homogeneity of liquid metal polymer composites: impact on mechanical, electrical, and sensing behavior

Non-Destructive Test Method for Diagnosing Turn-to-Turn Circuits of Electric Motor Windings Under Conditions of Local Reactive Power Compensation

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