A Fractional Fourier Transform-Based Approach for Gas-Insulated Switchgear Partial Discharge Recognition

Gu, Feng‐Chang; Chen, Hung-Cheng; Chen, Bo-Yan

doi:10.1007/s42835-019-00219-5

Cited by 9 publications

(2 citation statements)

References 29 publications

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“…In [46], Basharan et al extracted the average and standard deviation of FD and lacunarity from the 3-D PRPD plots, and high classification accuracy was achieved by using these fractal features. Authors in [47], [48], and [49] basically adopted similar scheme as in [46], the difference was that they used different kinds of PD spectrograms as the source images for fractal features extraction. To sum up, the fractal features can well reflect the characteristics of different PD spectrograms, thus can achieve satisfactory recognition results.…”

Section: ) Chaos/fractal-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: ) Chaos/fractal-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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“…Qikun Feng et al [30] has studied the effect of metal depression on concave sides of the basin-type insulator in GIS at different heights and radii of metal depression and observed that at high voltage conductor the electric field stress is higher than that of grounded enclosure. Feng-Chang Gu et al [31] has identified four GIS models with typical defects where the models include internal conductor of porcelain bushing at load side like Type 1: Conductor of porcelain bushing containing oil grease, Type 2: Gas tank containing metal particles, Type 3: Operation handle containing welding depression, Type 4: Metal ring with an abrasion defect. Tao Wen et al [32] has evaluated the effectiveness of on-site GIS equipment with insulation defects, based on the insulation characteristics of a coaxial cylinder structure SF6 gas gap with a conductive depression under the impulses of different dimensions resulted with 50% breakdown voltage in the voltage-time curve.…”

Section: Introductionmentioning

confidence: 99%

Study of electric field stress on the surface contour and at the triple junction in three phase GIS with FGM spacer under the depression defect

Pakalapati¹,

Gundavarapu

Duvvada³

et al. 2020

International Journal of Emerging Electric Power Systems

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Now days, the establishment of spacers is in wide usage in three-phase Gas Insulated Busduct (GIB) for providing mechanical support and better insulation to the conductors. The region of the intersection of SF6 gas, enclosure end and the spacer is one of the weakest links in GIB, so the major concentration is done on minimization of electric field stress at this junction by using Functionally Graded Material (FGM) technique. The other incidents of insulation failures are due to several defects like depression, delamination etc. reduces the dielectric strength of the spacers. In this paper, an FGM post type spacer has been designed for a three-phase GIB under depression and further electric field stress at Triple Junction (TJ) is reduced by introducing a metal insert (MI) nearer to the TJ. Several filler materials are used as doping materials for obtaining different permittivity values using FGM technique to achieve uniform electric field stress. Simulation is carried out for the designed spacer at various operating voltages with different types of FGM gradings. The effect of depression with different dimensions and positions is analyzed before and after inserting MI to the FGM post type spacer in three-phase GIB.

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Deep convolutional neural network for partial discharge monitoring system

Srivastava

Avasthi

2023

Advances in Engineering Software

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A Fractional Fourier Transform-Based Approach for Gas-Insulated Switchgear Partial Discharge Recognition

Cited by 9 publications

References 29 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

Study of electric field stress on the surface contour and at the triple junction in three phase GIS with FGM spacer under the depression defect

Deep convolutional neural network for partial discharge monitoring system

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