A novel 1DCNN and domain adversarial transfer strategy for small sample GIS partial discharge pattern recognition

Wang, Yanxin; Yan, Jing; Yang, Zhou; Wang, Jianhua; Geng, Yingsan

doi:10.1088/1361-6501/ac27e8

Cited by 21 publications

(13 citation statements)

References 29 publications

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“…In the same year, a research group simulated four different sources of PDs in a GIS controlled lab environment [34]. Varying the defect location for each of the artificial defects ensured the variability in the collected dataset.…”

Section: Pd Classification Using Time-series Waveformmentioning

confidence: 99%

Deep Learning in High Voltage Engineering: A Literature Review

et al. 2022

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Condition monitoring of high voltage apparatus is of much importance for the maintenance of electric power systems. Whether it is detecting faults or partial discharges that take place in high voltage equipment, or detecting contamination and degradation of outdoor insulators, deep learning which is a branch of machine learning has been extensively investigated. Instead of using hand-crafted manual features as an input for the traditional machine learning algorithms, deep learning algorithms use raw data as the input where the feature extraction stage is integrated in the learning stage, resulting in a more automated process. This is the main advantage of using deep learning instead of traditional machine learning techniques. This paper presents a review of the recent literature on the application of deep learning techniques in monitoring high voltage apparatus such as GIS, transformers, cables, rotating machines, and outdoor insulators.

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Section: Pd Classification Using Time-series Waveformmentioning

confidence: 99%

Deep Learning in High Voltage Engineering: A Literature Review

et al. 2022

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“…The fault types of GIS are mainly insulation faults and mechanical faults [3]. There have been many researches on the diagnosis methods for insulation faults [4][5][6][7], but there are few methods for diagnosing mechanical faults. Mechanical failures are generally caused by loose parts, which may cause abnormal vibrations [8].…”

Section: Introductionmentioning

confidence: 99%

Mechanical fault diagnosis of gas-insulated switchgear based on saliency feature of auditory brainstem response under noise background

Ji,

Liu,

Wang

et al. 2023

Meas. Sci. Technol.

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The mechanical fault of gas-insulated switchgear (GIS) seriously threatens the security of the power grid. Recently, acoustic-based fault diagnosis methods, which have the advantage of non-contact measurement, have been applied to the GIS mechanical fault diagnosis, but vulnerable to the interference of the background noise. To improve the capacity of the acoustic-based GIS fault diagnosis under noise background, by simulating the sound feature extraction ability and anti-noise ability of human auditory system, a novel GIS mechanical fault diagnosis method based on saliency feature of auditory brainstem response (SFABR) is proposed. First, an auditory saliency model, which considers both the auditory periphery and the auditory nerve center was constructed by combining the deep auditory model and the saliency model. After processing GIS emitted acoustic signal, the auditory brainstem response (ABR) was obtained, and the saliency features of the ABR were extracted to obtain the SFABR. Then, the characteristic frequency distribution of the auditory saliency model was adjusted to make it more suitable for the spectral characteristics of the GIS sound signal. Finally, the SFABR was mapped to a two-dimensional CNN to train a model for GIS mechanical fault diagnosis. This method simulates the process of auditory response extraction and the selection effect of auditory attention on sound elements. 110kV three-phase GIS fault simulation experiment shows that for GIS mechanical faults, the diagnosis method based on SFABR can obtain 96.1% fault identification accuracy. In different noise environments, compared with the traditional acoustic-based fault diagnosis methods, this method has stronger anti-noise performance, and can more effectively realize the identification of GIS mechanical failure types. In future research, the method can be further extended to fault diagnosis of more types of power equipment.

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“…First, a feature AM is introduced on the input side to quantify the relationship between performance parameters and blockchain output; then, through the powerful feature extraction capability of one-dimensional (One Dimensional, 1D-CNN), the local information between the input information is mined. Finally, a time-series feature AM is introduced on the output side to strengthen the expression of important information at historical moments for the prediction output [21]. The purpose is to establish a prediction model that depends on each performance parameter under the time node, explore the connection between mobile payment and blockchain, and accurately predict the impact of blockchain on mobile payment.…”

Section: Introductionmentioning

confidence: 99%

Research on the development and application of CNN model in mobile payment terminal and blockchain economy

Liu¹

2023

3C Empresa

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As the most widely used payment method at this stage, mobile payment is more and more closely related to the blockchain economy. Traditional methods lack a certain degree of accuracy. This research proposes a feature-based and sequential-based Bilateral AM (BAM) and Convolutional Neural Network (CNN)-gated recurrent unit for the development and application of mobile payment and blockchain economy (Gated Recurrent Unit, GRU) hybrid model (BAM-CNN-GRU), select 5 feature parameters with high correlation with the blockchain for multivariate prediction. The introduction of BAM can automatically quantify the correlation between the input variables and the blockchain, and strengthen the expression of historical key information on the predicted output; the introduction of CNN can extract high-dimensional features that reflect the non-stationary dynamic changes of the blockchain. The proposed hybrid model achieves good results in both single-step and multi-step long-term series and multivariate input blockchain prediction. Compared with the other six methods, MAE is reduced by 75.45%, 64.74%, 62.84%, respectively. 59.41%, 45.54%, 44.16%. Compared with the BAM-GRU model, the CNN-GRU model, the GRU model, the LSTM model, the support vector machine SVM model and the BP model, the prediction accuracy of the hybrid model has been greatly improved, and it has a broader application prospect.

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A novel 1DCNN and domain adversarial transfer strategy for small sample GIS partial discharge pattern recognition

Cited by 21 publications

References 29 publications

Deep Learning in High Voltage Engineering: A Literature Review

Deep Learning in High Voltage Engineering: A Literature Review

Mechanical fault diagnosis of gas-insulated switchgear based on saliency feature of auditory brainstem response under noise background

Research on the development and application of CNN model in mobile payment terminal and blockchain economy

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