Abstract
In order to enable many of the required smart grid functionalities, distribution systems are becoming increasingly dependent on state estimators. Many cyber-attacks attempt false data injection (FDI) attacks on such state estimators. The majority of the existing literature deal with FDIs in distribution systems state estimation either by the analysis of the residual vector elements, or by the analysis of historical data. In this work, we adopt an alternative approach for the detection of FDIs in distribution system state estimation, wherein FDIs are modelled as measurement biases and a bias filter is employed for FDI detection. Additionally, in order to enable the detection of time-variable FDIs, a failure detector is integrated in the recursive formulation of the bias filter, which is based on the Kalman filter. The developed approach is accordingly capable of identifying time-varying FDIs, which can evade many of the existing FDI detection methods. Simulation case studies are performed on the IEEE 13-node and 123-node feeders with different FDIs and the performance of the proposed approach is analyzed.
Highlights
Electric power distribution systems (DS) are currently moving towards an ever-increasing dependency on modern informatics and communication systems
Residual based bad data detection methods are widely used in the literature for the detection of false data in distribution system state estimation
A bias filter is formulated alternatively for the power distribution systems, and its superiority over the residual-based bad data detection (rBDD) is shown in theory and simulations
Summary
Electric power distribution systems (DS) are currently moving towards an ever-increasing dependency on modern informatics and communication systems. Recently several research articles proposed a new class of detection schemes [13]-[14] using the cumulative sum of the residual vector These schemes are based on a statistical method originally developed in [15], where the problem is to detect a time instant at which a set of sequential data changes from one probability distribution function (PDF) to another (i.e., before and after the attack). We propose a modified filter to generalize the failure detector integration and make it applicable for DS-SE In this way, the approach we are proposing in this work, guards all the advantages of the CBF, and can follow time-variable FDIs as they change with time. The proposed estimator enables online estimation of the simultaneous FDI values under a large intrusion level
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