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Cyber-Attack Identification of Synchrophasor Data Via VMD and Multifusion SVM by Wei Qiu; Kunzhi Zhu; Zhaosheng Teng; Qiu Tang; Wenxuan Yao; Yuqing Dong; Yilu Liu is a Engineering article available to read on EtoBox.

What is Cyber-Attack Identification of Synchrophasor Data Via VMD and Multifusion SVM about?

A large amount of synchrophasor data in the wide area measurement system (WAMS) needs to be collected and transmitted to the phasor data concentrator, thereby increasing the possibility of being attacked by hackers. The attacked data are therefore hidden into the normal synchrophasor data so that the synchrophasor data based application will be affected. To remedy this problem, an identification framework is proposed to detect the data cyber-attack in WAMS utilizing variational mode decomposition (VMD) and multifusion support vector machine (MSVM). First, VMD is used to transform the attacked data into multiple modal components. Thereafter, a novel MSVM is employed to classify the deterministic features using the proposed linear combined multikernel (LCM). This LCM can fuse multiple types of features, including the time, frequency, and statistical domains of the synchrophasor data. Utilizing the actual data from FNET/GridEye, different experiments are conducted under multiple attack strengths and types. The results demonstrate that the identification framework has higher precision and robustness compared with other conventional classifiers.

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Author

Wei Qiu; Kunzhi Zhu; Zhaosheng Teng; Qiu Tang; Wenxuan Yao; Yuqing Dong; Yilu Liu

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2022

Language

EN

Field

Engineering (Physical Sciences)