The Framework of Invariant Electric Vehicle Charging Network for Anomaly Detection

被引：4

作者：

Chung, Yu-Wei ^{[1
]}

Mathew, Mervin ^{[1
]}

Rodgers, Cole ^{[1
]}

Wang, Bin ^{[2
]}

Khaki, Behnam ^{[3
]}

Chu, Chicheng ^{[1
]}

Gadh, Rajit ^{[1
]}

机构：

[1] Univ Calif Los Angeles, Smart Grid Energy Res Ctr SMERC, Los Angeles, CA 90024 USA

[2] Lawrence Berkeley Natl Lab, Berkeley, CA USA

[3] New York Power Author, New York, NY USA

来源：

2020 IEEE TRANSPORTATION ELECTRIFICATION CONFERENCE & EXPO (ITEC) | 2020年

关键词：

Anomaly detection; correlation networks; EV charging scheduling; machine learning; time series analysis;

D O I：

10.1109/itec48692.2020.9161576

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Electric vehicle (EV) charging management systems control and schedule EV load according to the measurements of local building load, solar generation, and dynamic electricity price. Within this information network, any data replaced or modified by an attacker will disrupt the EV charging schedule and could cause damage to the electricity grid. Under real circumstances, these measurements are correlated in a way that is not true for false data. This paper examines the relationship of pairwise measures within the system to establish a correlation-invariant network, and a multivariate time-series segmentation method along with a weighted k nearest neighbor (kNN) classifier is proposed to detect the changes in correlations and identify anomalous data within the network.

引用

页码：631 / 636

页数：6

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