NDLI: GIS partial discharge pattern recognition via lightweight convolutional neural network in the ubiquitous power internet of things context

Content Provider	IET Digital Library
Author	Wang, Yanxin Yan, Jing Yang, Zhou Zhao, Yiming Liu, Tingliang
Abstract	The construction of the ubiquitous power internet of things (UPIoT) provides a new feasible solution for gas-insulated switchgear (GIS) online monitoring and fault diagnosis, but it also puts forward greater requirements for time and accuracy. How to find an effective real-time model that can be applied to the UPIoT mobile terminals has become an urgent problem needing to be solved. To this end, this study proposes a lightweight convolutional neural network (LCNN) for GIS partial discharge (PD) pattern recognition using three lightweight convolutional blocks, and introduces the lowest recognition accuracy of single-class faults as the primary indicator for selecting the optimal model under the UPIoT. First, three lightweight convolutional blocks are introduced for constructing an LCNN. Then, the optimal model constructed by the lightweight blocks is sought. Next, criteria for determining the best model are introduced, and the best model under the UPIoT is selected. This study provides a reference standard for the construction of GIS PD pattern recognition under the UPIoT. Meanwhile, through the balance of evaluation indicators, this study verifies that the minimum recognition accuracy of the MnasNet model is 98.8%, which is obviously better than other methods and lays a solid foundation for GIS PD pattern recognition.
Starting Page	864
Ending Page	871
Page Count	8
ISSN	17518822
Volume Number	14
e-ISSN	17518830
Issue Number	Issue 8, Oct (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-smt/14/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-smt.2019.0542
Journal	IET Science, Measurement & Technology
Publisher Date	2020-08-14
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Convolutional Neural Nets Dielectric Breakdown And Discharges Fault Diagnosis Gas Insulated Substations Gas Insulated Switchgear GIS Partial Discharge Pattern Recognition GIS PD Pattern Recognition Internet of Things LCNN Lightweight Blocks Lightweight Convolutional Blocks Lightweight Convolutional Neural Network MnasNet Mobile Computing Neural Computing Technique Partial Discharges Pattern Recognition Pervasive Computing Power Engineering Computing Single-class Fault Substations Switch Ubiquitous Ubiquitous Power Internet of Things UPIoT Mobile Terminals
Content Type	Text
Resource Type	Article
Subject	Atomic and Molecular Physics, and Optics Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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