Partial discharge detection using acoustic emission method for a waveguide functional high-voltage cast-resin dry-type transformer

Su, Chean-Cheng; Tai, Cheng‐Chi; Chen, Chien-Yi; Hsieh, J.C.; Chen, Jiann-Fuh

doi:10.1109/cmd.2008.4580339

Cited by 10 publications

(3 citation statements)

References 13 publications

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“…Figure 7 shows the structure of the encoder network, which comprises five convolutional blocks (each including a 3 × 3 convolution layer, a rectified linear unit activation and a max pooling layer) and a flatten layer with D f = 3584. After the encoder network, the projection head comprising a single hidden layer with D g = 256 and τ = 0.05 is used to apply nonlinear transformation and project h to optimize the supervised contrastive loss in (6).…”

Section: Resultsmentioning

confidence: 99%

“…Hence, PD detection in the early phase is very important [4]. Different methods have been developed to detect PDs, including the use of loop antennas, acoustic emissions, or different types of internal and external sensors [5][6][7][8]. Among these methods, ultra-high-frequency (UHF) sensors can capture a wide range of frequencies and effectively reduce noise [9].…”

Section: Introductionmentioning

confidence: 99%

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Supervised Contrastive Learning for Fault Diagnosis Based on Phase-Resolved Partial Discharge in Gas-Insulated Switchgear

Dang,

Ho,

Vo-Nguyen

et al. 2023

Energies

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Supervised contrastive learning (SCL) has recently emerged as an alternative to conventional machine learning and deep neural networks. In this study, we propose an SCL model with data augmentation techniques using phase-resolved partial discharge (PRPD) in gas-insulated switchgear (GIS). To increase the fault data for training, we employ Gaussian noise adding, Gaussian noise scaling, random cropping, and phase shifting for supervised contrastive loss. The performance of the proposed SCL was verified by four types of faults in the GIS and on-site noise using an on-line ultra-high-frequency (UHF) partial discharge (PD) monitoring system. The experimental results show that the proposed SCL achieves a classification accuracy of 97.28% and outperforms the other algorithms, including support vector machines (SVM), multilayer perceptron (MLP), and convolution neural networks (CNNs) in terms of classification accuracy, by 6.8%, 4.28%, 2.04%, respectively.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Supervised Contrastive Learning for Fault Diagnosis Based on Phase-Resolved Partial Discharge in Gas-Insulated Switchgear

Dang,

Ho,

Vo-Nguyen

et al. 2023

Energies

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“…More specifically, in the analysis of PDs, they are used extensively in the detection and localization of PDs in high voltage equipment such as rotating machinery and transformers [14,[20][21][22][23][24]. In fact, Piezoceramic films performing at high frequency (High Temperature Ultrasonic Transducers-HTUTs) have been developed by Industrial Materials Institute, National Research Council of Canada [25,26].…”

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confidence: 99%

Partial discharges location in power transformers using piezoceramic sensors

Danouj

Tahan

David

et al. 2021

IJECE

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The detection and the spatial localization of partial discharges in high-voltage electrical machines are considered as an effective method in predictive maintenance that can provide valuable information on the health of the insulation system and allow to determine accurately the location of the risky insulation elements, which in turn will avoid any premature equipment’s deterioration by scheduling preventive maintenance action. After confirming in a previous published paper the efficiency of a new generation of piezoceramics sensors (high temperature ultrasonic transducers) to detect and characterize partial discharges, we are going to investigate, in this work, a second potential of this technology to locate the partial discharge sources by relying on its ability to detect acoustic signals emitted by partial discharge sources. We will present experimental results, demonstrating the effectiveness of these sensors to locate partial discharges sources and, we will also present an algorithm for calculating the partial discharge foci, based on the acoustic wave flight time.

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