Image-Based Partial Discharge Identification in High Voltage Cables Using Hybrid Deep Network

被引:5
作者
Aldosari, Obaid [1 ]
Aldowsari, Mohammed A. [2 ]
Batiyah, Salem Mohammed [3 ]
Kanagaraj, N. [1 ]
机构
[1] Prince Sattam Bin Abdulaziz Univ, Coll Engn, Dept Elect Engn, Wadi Ad Dawasir 11991, Saudi Arabia
[2] King Khaled Univ, Coll Engn, Dept Elect Engn, Abha 62529, Saudi Arabia
[3] Yanbu Ind Coll, Dept Elect & Elect Engn Technol, Yanbu Ind, Al Madinah Al Munawwarah 46452, Saudi Arabia
关键词
Partial discharge; RGB; gray; data augmentation; LSTM; CNN; GAS-INSULATED SWITCHGEAR; RECOGNITION;
D O I
10.1109/ACCESS.2023.3278054
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Deep learning and digital image technologies have combined to create a potentially effective tool for identifying partial discharge (PD) patterns precisely. However, it is necessary to investigate which algorithm guarantees the best performance. The more common tools are restricted by a lack of training data and an advanced model in itself. Therefore, the main goal of this paper is to develop an efficient hybrid network comprising two deep networks, long short-term memory (LSTM), and convolutional neural network (CNN), for identifying the form of PD. A total of 8186x25 (non-PDxPD) images were applied to assess the proposed methods. The size of the PD type was increased to 3675 images using data augmentation techniques. The results indicated that the integration of CNN and LSTM networks can provide a more robust implementation for PD detection. The integrated CNN-LSTM deep network based on data augmentation outperformed features derived from a single deep network. The recall, F-measure, and classification precision have 99.9% as a validation accuracy with a 99.8% intersection over union and a loss of 0.004.
引用
收藏
页码:50325 / 50333
页数:9
相关论文
共 23 条
  • [1] Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking
    Bae, Seung-Hwan
    Yoon, Kuk-Jin
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2018, 40 (03) : 595 - 610
  • [2] Partial Discharge Classification in Power Electronics Applications using Machine Learning
    Balouji, Ebrahim
    Hammarstrom, Thomas
    McKelvey, Tomas
    [J]. 2019 7TH IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (IEEE GLOBALSIP), 2019,
  • [3] Albumentations: Fast and Flexible Image Augmentations
    Buslaev, Alexander
    Iglovikov, Vladimir I.
    Khvedchenya, Eugene
    Parinov, Alex
    Druzhinin, Mikhail
    Kalinin, Alexandr A.
    [J]. INFORMATION, 2020, 11 (02)
  • [4] Partial Discharge Source Discrimination using a Support Vector Machine
    Hao, L.
    Lewin, P. L.
    [J]. IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION, 2010, 17 (01) : 189 - 197
  • [5] Toward automatic classification of partial discharge sources with neural networks
    Hirata, A
    Nakata, S
    Kawasaki, ZI
    [J]. IEEE TRANSACTIONS ON POWER DELIVERY, 2006, 21 (01) : 526 - 527
  • [6] Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI [10.1162/neco.1997.9.1.1, 10.1007/978-3-642-24797-2]
  • [7] Partial Discharge Online Detection for Long-Term Operational Sustainability of On-Site Low Voltage Distribution Network Using CNN Transfer Learning
    Kim, Jinseok
    Kim, Ki-Il
    [J]. SUSTAINABILITY, 2021, 13 (09)
  • [8] Partial Discharge Recognition with a Multi-Resolution Convolutional Neural Network
    Li, Gaoyang
    Wang, Xiaohua
    Li, Xi
    Yang, Aijun
    Rong, Mingzhe
    [J]. SENSORS, 2018, 18 (10)
  • [9] A Novel GIS Partial Discharge Detection Sensor With Integrated Optical and UHF Methods
    Li, Junhao
    Han, Xutao
    Liu, Zehui
    Yao, Xiu
    [J]. IEEE TRANSACTIONS ON POWER DELIVERY, 2018, 33 (04) : 2047 - 2049
  • [10] Partial Discharge Recognition in Gas Insulated Switchgear Based on Multi-information Fusion
    Li, Liping
    Tang, Ju
    Liu, Yilu
    [J]. IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION, 2015, 22 (02) : 1080 - 1087