Abstract
Power system state estimation is an important component of the status and healthiness of the underlying electric power grid real-time monitoring. However, such a component is prone to cyber-physical attacks. The majority of research in cyber-physical power systems security focuses on detecting measurements False-Data Injection attacks. While this is important, measurement model parameters are also a most important part of the state estimation process. Measurement model parameters though, also known as static-data, are not monitored in real-life applications. Measurement model solutions ultimately provide estimated states. A state-of-the-art model presents a two-step process towards simultaneous false-data injection security: detection and correction. Detection steps are χ2 statistical hypothesis test based, while correction steps consider the augmented state vector approach. In addition, the correction step uses an iterative solution of a relaxed non-linear model with no guarantee of optimal solution. This paper presents a linear programming method to detect and correct cyber-attacks in the measurement model parameters. The presented bi-level model integrates the detection and correction steps. Temporal and spatio characteristics of the power grid are used to provide an online detection and correction tool for attacks pertaining the parameters of the measurement model. The presented model is implemented on the IEEE 118 bus system. Comparative test results with the state-of-the-art model highlight improved accuracy. An easy-to-implement model, built on the classical weighted least squares solution, without hard-to-derive parameters, highlights potential aspects towards real-life applications.
Highlights
Where z ∈ Rm is the measurement vector, x ∈ R N is the state variables vector, h(x):Rm → R N, (m > N ) is a non-linear differentiable function that relates the states to the measurements, e is the measurement error vector assumed with zero mean, standard deviation σ and having Gaussian probability distribution, and N = 2n − 1 is the number of unknown state variables and n is the number of buses in the system
This paper presents a bi-level model for correcting parameter False Data Injection (FDI) cyber-attacks on the State Estimation (SE) process
The presented model combines the two processed that are usually performed by SE for detection and correction into a single process for parameter attack processing
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Measurement model parameters cyber-attacks, on the other hand, have limited research in the field of power systems These parameters are considered static and without error during the SE process. While it is effective to have such a state estimator in a single level model, and eliminating post-processing detection algorithms, the work in [26] assumed errors in parameters are varied in a small range, not considering the possibility of R2U and U2R attacks that enables an adversary to alter those parameters in any range. A simultaneous cyber-attack detection and correction bi-level model is presented, towards the solution of previously mentioned state-of-the-art limitations. An explicit mathematical bi-level model for detecting and correcting cyber-attack pertaining state estimator static data.
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