Research and application of intermittent partial discharge characteristics and easy-warning system for electric equipment

Niu, Bo; Ma, Feiyue; Wu, Xutao; Ni, Hui; Zhou, Xinbo; Wu, Hong

doi:10.1016/j.egyr.2022.05.150

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Substation is an important part of the transmission system, substation contains primary equipment and secondary equipment, power transformer is a primary equipment in the role of transmitting and distributing electric energy power equipment, is an important part of the substation can affect the normal operation, it has great significance for the stability of the whole power grid to prevent power transformer failure. [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Research on Transformer Partial Discharge Feature Extraction Based on Empirical Wavelet Transform and Multiscale Permutation Entropy

Feng

Liu

et al. 2023

J. Phys.: Conf. Ser.

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Aiming to extract efficiently the fault features of partial discharge in the process of fault diagnosis of power transformer, a method of combining Empirical Wavelet Transform (EWT) with Multiscale Permutation Entropy (MPE) is advanced to extract fault features of transformers partial discharge. Firstly, four different partial discharge pulse signals are analyzed by EWT method, and the fault signal is decomposed according to different frequency domain characteristics of the signal to obtain the intrinsic mode function (IMF) of the signal. Then, the calculated multi-scale permutation entropy of different IMFs to complete the fault feature extraction. Finally, the multi-scale entropy of the fault semaphore is used as the eigenvector of the Support Vector Machine (SVM) for glitch diagnosis, and the accurate systematization of the partial discharge semaphore of the transformer is realized. semaphore Compared with the Continuous Wavelet Transform (CWT), Empirical Mode Decomposition (EMD), and Ensemble Empirical Mode Decomposition (EEMD) feature extraction way, it shows that the raised EWT-MPE is more valid and accurate in diagnosing and analyzing transformer partial discharge faults, and the accuracy of fault classification 96.43%.

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Section: Introductionmentioning

confidence: 99%

Research on Transformer Partial Discharge Feature Extraction Based on Empirical Wavelet Transform and Multiscale Permutation Entropy

Feng

Liu

et al. 2023

J. Phys.: Conf. Ser.

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Partial Discharge Distributed Locating in Power Cable Joints Using Φ-OTDR Optical Fiber System

Liu,

Yang,

Chen

et al. 2024

IEEE Sensors J.

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Partial Discharge Data Enhancement and Pattern Recognition Method Based on a CAE-ACGAN and ResNet

Li,

Wang,

Han

et al. 2024

Symmetry

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In order to address the issue of the accuracy of partial discharge pattern recognition being constrained by unbalanced samples and the deep structure of the deep learning network, a method for partial discharge data enhancement and pattern recognition based on a convolutional autoencoder auxiliary classifier generative adversarial network (CAE-ACGAN) and a residual network (ResNet) is proposed. The initial step involves the preprocessing of the acquired partial discharge signals, with the phase resolved partial discharge (PRPD) spectra subsequently employed as the training samples. Secondly, a CAE-ACGAN is constructed. The model combines the advantages of a deep convolutional self-coding structure and a generative adversarial paradigm to generate high-quality phase resolved partial discharge spectrograms. Subsequently, a ResNet is employed as the classifier for partial discharge pattern recognition, utilising the CAE-ACGAN-enhanced partial discharge dataset for network training to achieve accurate recognition of partial discharge signals. The experimental findings demonstrate that the SSIM and PSNR indexes of the CAE-ACGAN model utilised in this study are 0.92 and 45.88 dB, respectively. The partial discharge pattern method employing the CAE-ACGAN and ResNet exhibits superiority in identifying partial discharges, attaining an identification accuracy of 98%, which is 7.25% higher than the pre-enhancement level.

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Research and application of intermittent partial discharge characteristics and easy-warning system for electric equipment

Cited by 3 publications

References 12 publications

Research on Transformer Partial Discharge Feature Extraction Based on Empirical Wavelet Transform and Multiscale Permutation Entropy

Research on Transformer Partial Discharge Feature Extraction Based on Empirical Wavelet Transform and Multiscale Permutation Entropy

Partial Discharge Distributed Locating in Power Cable Joints Using Φ-OTDR Optical Fiber System

Partial Discharge Data Enhancement and Pattern Recognition Method Based on a CAE-ACGAN and ResNet

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