Open AccessGIS partial discharge pattern recognition via lightweight convolutional neural network in the ubiquitous power internet of things context
Author(s) -
Wang Yanxin,
Yan Jing,
Yang Zhou,
Zhao Yiming,
Liu Tingliang
Publication year - 2020
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2019.0542
Subject(s) - computer science , switchgear , partial discharge , convolutional neural network , context (archaeology) , data mining , pattern recognition (psychology) , artificial intelligence , real time computing , machine learning , engineering , voltage , electrical engineering , paleontology , biology
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.