Joint State Estimation and Cyber-Attack Detection Based on Feature Grouping

Inspite of the significant development in network security, the existing solutions are unable to completely defend Smart grid against the malicious threats. Smart grid technology increases the reliability, security and efficiency of the electrical grid. However, its strong dependencies on digital communication technology brings up new vulnerabilities that need to be considered for efficient and reliable power distribution. This paper proposes an anomaly detection techniques based on feature grouping combined with linear correlation coefficient (FGLCC) algorithm. Decision tree is used as the classifier in the proposed method. For performance verification, the proposed method was applied on IEEE 39-bus system. The results verified a high accuracy (96%) and detection rate (97%) with a low false positive rate (1.65%) compared to the existing methods in the literature.