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Haotian Shi, Lin Cheng, Lanjun Yang
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Partial discharge classifications: Review of recent progress
10.1016/j.measurement.2015.02.032 · 2015
Condition monitoring based on partial discharge diagnostics using machine learning methods: A comprehensive state-of-the-art review
10.1109/tdei.2020.009070 · 2020
A review on partial discharge diagnosis in cables: Theory, techniques, and trends
10.1016/j.measurement.2023.112882 · 2023
Surface charge accumulation and pre-flashover characteristics induced by metal particles on the insulator surfaces of 1100 kV GILs under AC voltage
10.1049/hve.2019.0222 · 2020
Partial discharge pattern recognition of power cable joints using extension method with fractal feature enhancement
10.1016/j.eswa.2011.08.140 · 2012
A novel extension neural network based partial discharge pattern recognition method for high-voltage power apparatus
10.1016/j.eswa.2011.09.030 · 2012
Partial discharge classification using deep learning methods—Survey of recent progress
10.3390/en12132485 · 2019
Noise invariant partial discharge classification based on convolutional neural network
10.1016/j.measurement.2021.109220 · 2021
Multimodal fusion and Domain-Invariant feature learning for partial discharge detection
2025
Deep learning and data augmentation for partial discharge detection in electrical machines
10.1016/j.engappai.2024.108074 · 2024
Deep residual learning for image recognition
2016
Combining Multi-Level feature extraction algorithm with residual graph convolutional neural network for partial discharge detection
10.1016/j.measurement.2024.116151 · 2025
End-to-End Multi-Scale residual network with parallel attention mechanism for fault diagnosis under noise and small samples
10.1016/j.isatra.2024.12.023 · 2025
Denoising of partial discharge signal using a hybrid framework of total variation denoising-autoencoder
10.1016/j.measurement.2023.113674 · 2023
Comparative analysis of machine learning and deep learning techniques on classification of artificially created partial discharge signal
10.1016/j.measurement.2024.114947 · 2024
Multi-source partial discharge diagnosis in gas-insulated switchgear via zero-shot learning
10.1016/j.measurement.2023.113033 · 2023
Partial discharge data enhancement and pattern recognition method based on a CAE-ACGAN and ResNet
10.3390/sym17010055 · 2024
Artificial intelligence based partial discharge detection using CNN and KNN to increase the quality of electrical insulation
10.1007/s10791-025-09624-z · 2025
Impact of impulse voltage frequency on the partial discharge characteristic of electric vehicles motor insulation
10.1016/j.engfailanal.2020.104767 · 2020
Partial discharge behavior and insulation failures detection in electrical devices subjected to impulse voltage excitation
10.1016/j.ijepes.2025.111078 · 2025
A novel federated transfer learning framework for intelligent diagnosis of insulation defects in Gas-Insulated switchgear
2022
Self-supervised asynchronous federated learning for diagnosing partial discharge in Gas-Insulated switchgear
10.3390/en18123078 · 2025
Federated Learning-Based distributed model predictive control
10.1016/j.jprocont.2025.103472 · 2025
Federated Learning-Based Offset-Free distributed control of nonlinear networked systems with application to IIoT
10.1109/tnse.2025.3540643 · 2025
Personalized federated learning-based distributed model predictive control with predictive error compensation for nonlinear networked systems
10.1109/tase.2025.3608014 · 2025
Optimal energy management of buildings using neural Network-Based thermal prediction and economic model predictive control
10.1016/j.aei.2025.104278 · 2026
Optimal energy management of buildings using neural Network-Based thermal prediction and economic model predictive control
10.1016/j.aei.2025.104278 · doi-reference
Personalized federated learning-based distributed model predictive control with predictive error compensation for nonlinear networked systems
10.1109/tase.2025.3608014 · doi-reference
Federated Learning-Based Offset-Free distributed control of nonlinear networked systems with application to IIoT
10.1109/tnse.2025.3540643 · doi-reference
Federated Learning-Based distributed model predictive control
10.1016/j.jprocont.2025.103472 · doi-reference
Self-supervised asynchronous federated learning for diagnosing partial discharge in Gas-Insulated switchgear
10.3390/en18123078 · doi-reference
Partial discharge behavior and insulation failures detection in electrical devices subjected to impulse voltage excitation
10.1016/j.ijepes.2025.111078 · doi-reference
Impact of impulse voltage frequency on the partial discharge characteristic of electric vehicles motor insulation
10.1016/j.engfailanal.2020.104767 · doi-reference
Artificial intelligence based partial discharge detection using CNN and KNN to increase the quality of electrical insulation
10.1007/s10791-025-09624-z · doi-reference
Partial discharge data enhancement and pattern recognition method based on a CAE-ACGAN and ResNet
10.3390/sym17010055 · doi-reference
Multi-source partial discharge diagnosis in gas-insulated switchgear via zero-shot learning
10.1016/j.measurement.2023.113033 · doi-reference
Comparative analysis of machine learning and deep learning techniques on classification of artificially created partial discharge signal
10.1016/j.measurement.2024.114947 · doi-reference
Denoising of partial discharge signal using a hybrid framework of total variation denoising-autoencoder
10.1016/j.measurement.2023.113674 · doi-reference
End-to-End Multi-Scale residual network with parallel attention mechanism for fault diagnosis under noise and small samples
10.1016/j.isatra.2024.12.023 · doi-reference
Combining Multi-Level feature extraction algorithm with residual graph convolutional neural network for partial discharge detection
10.1016/j.measurement.2024.116151 · doi-reference
Deep learning and data augmentation for partial discharge detection in electrical machines
10.1016/j.engappai.2024.108074 · doi-reference
Noise invariant partial discharge classification based on convolutional neural network
10.1016/j.measurement.2021.109220 · doi-reference
Partial discharge classification using deep learning methods—Survey of recent progress
10.3390/en12132485 · doi-reference
A novel extension neural network based partial discharge pattern recognition method for high-voltage power apparatus
10.1016/j.eswa.2011.09.030 · doi-reference
Partial discharge pattern recognition of power cable joints using extension method with fractal feature enhancement
10.1016/j.eswa.2011.08.140 · doi-reference
Surface charge accumulation and pre-flashover characteristics induced by metal particles on the insulator surfaces of 1100 kV GILs under AC voltage
10.1049/hve.2019.0222 · doi-reference
A review on partial discharge diagnosis in cables: Theory, techniques, and trends
10.1016/j.measurement.2023.112882 · doi-reference
Condition monitoring based on partial discharge diagnostics using machine learning methods: A comprehensive state-of-the-art review
10.1109/tdei.2020.009070 · doi-reference
Partial discharge classifications: Review of recent progress
10.1016/j.measurement.2015.02.032 · doi-reference