Open AccessFault Feature Selection for Distribution Network
Author(s) -
Zhiqiang Wu,
Lizong Zhang,
Gang Yu,
Ying Wang,
Tao Huang,
Yanfang Zhou
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1881/2/022080
Subject(s) - fault (geology) , fault indicator , fault detection and isolation , stuck at fault , fault coverage , feature selection , feature (linguistics) , computer science , fault model , power (physics) , engineering , real time computing , pattern recognition (psychology) , artificial intelligence , electronic circuit , electrical engineering , linguistics , philosophy , seismology , actuator , geology , physics , quantum mechanics
For the connection of DGs in distribution network, the fault power flow is different from that in normal operation. Further, the size of the fault current is limited by the electronic components and greatly reduces. Therefore, fault detection, the protections and their coordination become very complex. Fault detection technology helps to achieve fault isolation and recovery, and plays an important role in distribution network control and operation. This paper proposes a data-driven fault feature selection method for distribution network. This method collects various electrical quantities during normal and short-circuit faults of the distribution network as a feature library, and uses the support vector machine-recursive feature elimination method for feature selection to remove redundant features. The optimized fault features can be used to fault detection for distribution network with DGs.