A PMU-based Multivariate Model for Classifying Power System Events

TL;DR

This paper introduces a multivariate text mining-based model for real-time detection of false data and classification of power system transient events using PMU data, enhancing system reliability and situational awareness.

Contribution

It presents a novel multivariate approach that effectively detects false data and classifies transient events regardless of system topology or PMU placement.

Findings

Effective false data detection across different system conditions

Accurate transient event classification on IEEE 30-bus system

Robust performance regardless of PMU coverage and placement

Abstract

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.