Remote Condition Monitoring of HVDC Cable Insulation Using Deep Learning-Aided Space Charge Classification

被引:7
作者
Roy, Sayanjit Singha [1 ]
Paramane, Ashish [1 ]
Singh, Jiwanjot [1 ]
Meng, Fanbo [2 ]
Dai, Chao [2 ]
Das, Arup Kumar [3 ]
Chatterjee, Soumya [4 ]
Chen, Xiangrong [2 ,5 ,6 ]
Tanaka, Yasuhiro [7 ]
机构
[1] NIT Silchar, Elect Engn Dept, Silchar 788010, Assam, India
[2] Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Peoples R China
[3] Univ Engn & Management, Dept Basic Sci & Humanities, Kolkata 700160, W Bengal, India
[4] NIT Durgapur, Dept Elect Engn, Durgapur 713209, W Bengal, India
[5] Zhejiang Univ, Hangzhou Global Sci & Technol Innovat Ctr, Int Campus, Haining 314400, Peoples R China
[6] Zhejiang Univ, Adv Elect Int Res Ctr, Int Campus, Haining 314400, Peoples R China
[7] Tokyo City Univ, Measurement & Elect Machine Control Lab, Setagaya Ku, Tokyo 1588557, Japan
基金
中国国家自然科学基金;
关键词
Cable insulation; convolutional neural networks (CNNs); deep learning (DL); high voltage dc (HVdc); space charge; NEURAL-NETWORK; XLPE; ACCUMULATION;
D O I
10.1109/TDEI.2022.3214171
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The homo-charges accumulated inside the polymeric insulation of high voltage dc (HVdc) cables increase the electrical field beyond the uniform value, whereas the hetero-charges reduce it. Such uneven electrical field distribution is detrimental to HVdc cables' longevity and thus, needs to be identified accurately. To this end, this article proposes a deep learning (DL) framework for the automated detection of space charges in HVdc cable insulation. The space charges inside the cross-linked polyethylene (XLPE) insulation samples were measured under varying electric fields (10-50 kV/mm) as well as at different temperatures (30 degrees C-70 degrees C) conditions. The obtained space charge profiles corresponding to no charge, hetero-charge, and homo-charge categories were fed to two benchmark convolutional neural network (CNN) models, i.e., AlexNet and VGGNet16, for automated feature extraction and classification purpose. The convolutional blocks of the two CNN models were sequentially fine-tuned using transfer learning (TL) to observe the variation in classificationaccuracies. The proposed approach delivers appreciably high classification accuracies irrespective of varying electric field and temperature conditions with reduced computational time toward distinguishing different categories of space charge accumulations compared to other CNN models. Hence, it can be potentially used for real-time HVdc insulation diagnostic purposes.
引用
收藏
页码:377 / 384
页数:8
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