Design of Pattern Recognition Application of Partial Discharge Signals Using Artificial Neural Networks

Lumba, Lunnetta Safura; Khayam, Umar; Maulana, Rian

doi:10.1109/iceei47359.2019.8988895

Cited by 12 publications

(2 citation statements)

References 1 publication

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“…The comparison results are shown in Table 1. We compared the following recognition methods: BPNN [42]: It is an applied method for identifying types of PDs by using artificial neural networks (ANNs) The authors utilize this branch of ANN for improved diagnosis of high-voltage devices, which is performed by observing the phase pattern of PD signals. The PD signals are evaluated by observing the maximum amplitude of positive and negative PDs and the number of times PDs appear in each cycle.…”

Section: Comparative Identification Experimentsmentioning

confidence: 99%

PMSNet: Multiscale Partial-Discharge Signal Feature Recognition Model via a Spatial Interaction Attention Mechanism

Deng,

Liu,

Zhu

et al. 2024

Sensors

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Partial discharge (PD) is a localized discharge phenomenon in the insulator of electrical equipment resulting from the electric field strength exceeding the local dielectric breakdown electric field. Partial-discharge signal identification is an important means of assessing the insulation status of electrical equipment and critical to the safe operation of electrical equipment. The identification effect of traditional methods is not ideal because the PD signal collected is subject to strong noise interference. To overcome noise interference, quickly and accurately identify PD signals, and eliminate potential safety hazards, this study proposes a PD signal identification method based on multiscale feature fusion. The method improves identification efficiency through the multiscale feature fusion and feature aggregation of phase-resolved partial-discharge (PRPD) diagrams by using PMSNet. The whole network consists of three parts: a CNN backbone composed of a multiscale feature fusion pyramid, a down-sampling feature enhancement (DSFB) module for each layer of the pyramid to acquire features from different layers, a Transformer encoder module dominated by a spatial interaction–attention mechanism to enhance subspace feature interactions, a final categorized feature recognition method for the PRPD maps and a final classification feature generation module (F-Collect). PMSNet improves recognition accuracy by 10% compared with traditional high-frequency current detection methods and current pulse detection methods. On the PRPD dataset, the validation accuracy of PMSNet is above 80%, the validation loss is about 0.3%, and the training accuracy exceeds 85%. Experimental results show that the use of PMSNet can greatly improve the recognition accuracy and robustness of PD signals and has good practicality and application prospects.

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Section: Comparative Identification Experimentsmentioning

confidence: 99%

PMSNet: Multiscale Partial-Discharge Signal Feature Recognition Model via a Spatial Interaction Attention Mechanism

Deng,

Liu,

Zhu

et al. 2024

Sensors

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show abstract

“…PD classification is a complex task, without a single correct solution, and various implementations of ML techniques and PD descriptions are proposed. Artificial Neural Networks have been tested with good results with classification methodologies that focus mostly on pattern recognition within the PRPD [26], [27] and its statistical features [28], [29]. Approaches using the time-domain recording to classify individual pulses have also been made with the pulse statistical and waveform features [30], [31] or using various dimensionality reduction techniques, such as PCA [32].…”

Section: Machine Learning In Pd Detectionmentioning

confidence: 99%

General Machine Learning-Based Approach to Pulse Classification for Separation of Partial Discharges and Interference

Ogliari,

Sakwa,

Wei

et al. 2023

IEEE Sensors J.

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This paper describes a complete approach to filtering Partial Discharge (PD) pulses from interference in High Voltage electrical equipment using supervised Machine Learning (ML) techniques. The PD signals are registered in Ultra High Frequency radiation band with a multisensor acquisition system composed of 4 antennae. The proposed methodology focuses on the implementation ML algorithms and proposes a novel in this field approach to the onset detection of incoming signals. The goal was to achieve high accuracy of filtering with reasonably low compilation times of the ML classifier. That would allow to use the model on edge sensor devices. In the paper, different models and training variants of the ML framework are tested. The presented results are based on a robust measurement campaign performed in laboratories of GEIRI Europe. The methodology is validated through tests on 3 separate test scenarios. Each represents a different complexity of the problem with an increasing number of active sources. The results show high potential for utilization of the ANN and other classifiers for PD filtering problems as the accuracy achieves the desired threshold of 80% for most of the tested variants. The methodology is a step forward toward a fully online PD and interference filter.

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A review of the applications of machine learning in the condition monitoring of transformers

Samimi

2022

Energy Syst

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Design of Pattern Recognition Application of Partial Discharge Signals Using Artificial Neural Networks

Cited by 12 publications

References 1 publication

PMSNet: Multiscale Partial-Discharge Signal Feature Recognition Model via a Spatial Interaction Attention Mechanism

PMSNet: Multiscale Partial-Discharge Signal Feature Recognition Model via a Spatial Interaction Attention Mechanism

General Machine Learning-Based Approach to Pulse Classification for Separation of Partial Discharges and Interference

A review of the applications of machine learning in the condition monitoring of transformers

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