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

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

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  • NETL
  • NSTL
  • Inst Engineering Technology-Iet
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.

pattern recognitionpartial dischargespower engineering computinggas insulated switchgeargas insulated substationsfault diagnosisconvolutional neural netsInternet of Thingsmobile computingGIS partial discharge pattern recognitionlightweight convolutional neural networkubiquitous power Internet of thingsfault diagnosisUPIoT mobile terminalsLCNNlightweight convolutional blockssingle-class faultslightweight blocksGIS PD pattern recognitionMnasNet

Wang, Yanxin、Yan, Jing、Yang, Zhou、Zhao, Yiming、Liu, Tingliang

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Xi An Jiao Tong Univ, State Key Lab Elect Insulat & Power Equipment, Xian, Peoples R China

Xi An Jiao Tong Univ, Sch Comp Sci, Xian, Peoples R China

2020

10.1049/iet-smt.2019.0542

IET science, measurement & technology

IET science, measurement & technology

ISSN：1751-8822
年,卷(期)：2020.14(8)
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