Diagnosis of Anomaly in the Dynamic State Estimator of a Power System using System Decomposition

TL;DR

This paper introduces a method using system decomposition to differentiate between malicious sensor data and modeling errors in power system state estimation, improving anomaly diagnosis accuracy.

Contribution

It proposes a novel approach employing Kalman decomposition to distinguish between malicious data and modeling errors in power system state estimators.

Findings

Successfully distinguishes between malicious data and modeling errors

Uses system decomposition to improve anomaly detection accuracy

Numerical results validate the effectiveness of the proposed method

Abstract

In a state estimator, the presence of malicious or simply corrupt sensor data or bad data is detected by the high value of normalized measurement residuals that exceeds the threshold value, determined by the $\chi^2$ distribution. However, high normalized residuals can also be caused by another type of anomaly, namely gross modeling or topology error. In this paper we propose a method to distinguish between these two sources of anomalies - 1) malicious sensor data and 2) modeling error. The anomaly detector will start with assuming a case of malicious data and suspect some of the individual measurements corresponding to the highest normalized residuals to be `malicious', unless proved otherwise. Then, choosing a change of basis, the state space is transformed and decomposed into `observable' and `unobservable' parts with respect to these `suspicious' measurements. We argue that, while the anomaly due to malicious data can only affect the `observable' part of the states, there exists no such restriction for anomalies due to modeling error. Numerical results illustrate how the proposed anomaly diagnosis based on Kalman decomposition can successfully distinguish between the two types of anomalies.