A PMU-based Multivariate Model for Classifying Power System Events
For power system operators, this work addresses the joint problem of false data detection and event classification, but results are limited to a small test system and lack quantitative comparison.
The paper proposes a multivariate model based on text mining to detect false data injection attacks and classify transient events using PMU data, achieving efficient detection and classification regardless of system topology, loading conditions, and PMU placement, as demonstrated on the IEEE 30-bus system.
Real-time transient event identification is essential for power system situational awareness and protection. The increased penetration of Phasor Measurement Units (PMUs) enhance power system visualization and real time monitoring and control. However, a malicious false data injection attack on PMUs can provide wrong data that might prompt the operator to take incorrect actions which can eventually jeopardize system reliability. In this paper, a multivariate method based on text mining is applied to detect false data and identify transient events by analyzing the attributes of each individual PMU time series and their relationship. It is shown that the proposed approach is efficient in detecting false data and identifying each transient event regardless of the system topology and loading condition as well as the coverage rate and placement of PMUs. The proposed method is tested on IEEE 30-bus system and the classification results are provided.