Open AccessAbnormal Detection System Design of Charging Pile Based on Machine Learning
Author(s) -
Yanjie Li,
Xiaoyu Ji,
Desheng Jiang,
Tao Meng
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/772/1/012058
Subject(s) - anomaly detection , grid , computer security , electric power system , power (physics) , computer science , control (management) , consumption (sociology) , power consumption , energy (signal processing) , power grid , automotive engineering , engineering , artificial intelligence , social science , statistics , physics , geometry , mathematics , quantum mechanics , sociology
With the exhaustion of fossil energy and people’s increasing attention to environmental protection, electric vehicles began to be popularized around the world. As an important infrastructure, the EV charging network is faced with the risk of a series of network attacks, which may cause economic losses to the power grid and car owners, and even endanger the stable operation of the power grid. In order to solve the security problem of charging piles, we designed an abnormal detection system for charging piles based on the power consumption side channel and machine learning. By collecting power consumption information of the charging control unit of charging piles, the abnormal detection system determines whether charging piles are facing attacks or not. We have verified three kinds of attacks, proving that our anomaly detection system can effectively detect attacks and protect the security and stable operation of charging piles.