Distributed Models for Sparse Attack Construction and State Vector Estimation in the Smart Grid

Date

2012-11-08

Author

Ozay, Mete
Esnaola, Inaki
Yarman Vural, Fatoş Tunay
Kulkarni, Sanjeev R.
Poor, H. Vincent

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

275
views

0
downloads

Two distributed attack models and two distributed state vector estimation methods are introduced to handle the sparsity of smart grid networks in order to employ unobservable false data injection attacks and estimate state vectors. First, Distributed Sparse Attacks in which attackers process local measurements in order to achieve consensus for an attack vector are introduced. In the second attack model, called Collective Sparse Attacks, it is assumed that the topological information of the network and the measurements is available to attackers. However, attackers employ attacks to the groups of state vectors. The first distributed state vector estimation method, called Distributed State Vector Estimation, assumes that observed measurements are distributed in groups or clusters in the network. The second method, called Collaborative Sparse State Vector Estimation, consists of different operators estimating subsets of state variables. Therefore, state variables are assumed to be distributed in groups and accessed by the network operators locally. The network operators compute their local estimates and send the estimated values to a centralized network operator in order to update the estimated values.

Subject Keywords

Smart grid security, Distributed optimization, Sparse models, Attack detection

URI

https://hdl.handle.net/11511/54506

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Machine Learning Methods for Attack Detection in the Smart Grid Ozay, Mete; Esnaola, Inaki; Yarman Vural, Fatoş Tunay; Kulkarni, Sanjeev R.; Poor, H. Vincent (2016-08-01) Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known...
Smarter Security in the Smart Grid Ozay, Mete; Esnaola, Inaki; Yarman Vural, Fatoş Tunay; Kulkarni, Sanjeev R.; Poor, H. Vincent (2012-11-08) A new formulation for detection of false data injection attacks in the smart grid is introduced. The attack detection problem is posed as a statistical learning problem in which the observed measurements are classified as being either attacked or secure. The proposed approach provides an attack detection framework that surmounts over the constraints arising due to the sparse structure of the problem and implicitly exploits any available prior knowledge about the system. Specifically, three supervised learni...
Collaborative Direction of Arrival estimation by using Alternating Direction Method of Multipliers in distributed sensor array networks employing Sparse Bayesian Learning framework Nurbas, Ekin; Onat, Emrah; Tuncer, Temel Engin (2022-10-01) In this paper, we present a new method for Direction of Arrival (DoA) estimation in distributed sensor array networks by using Alternating Direction Method of Multipliers (ADMM) in Sparse Bayesian Learning (SBL) framework. Our proposed method, CDoAE, has certain advantages compared to previous distributed DoA estimation methods. It does not require any special array geometry and there is no need for inter -array frequency and phase matching. CDoAE uses the distributed ADMM to update the parameter set extrac...
Sparse Attack Construction and State Estimation in the Smart Grid: Centralized and Distributed Models Ozay, Mete; Esnaola, Inaki; Yarman Vural, Fatoş Tunay; Kulkarni, Sanjeev R.; Poor, H. Vincent (Institute of Electrical and Electronics Engineers (IEEE), 2013-07-01) New methods that exploit sparse structures arising in smart grid networks are proposed for the state estimation problem when data injection attacks are present. First, construction strategies for unobservable sparse data injection attacks on power grids are proposed for an attacker with access to all network information and nodes. Specifically, novel formulations for the optimization problem that provide a flexible design of the trade-off between performance and false alarm are proposed. In addition, the ce...
Distributed area partitioning for multi-robot coverage Hocaoğlu, Burak; Şahin, Erol; Department of Computer Engineering (2022-5-9) This thesis focuses on the distributed coverage area partitioning problem in a robotic swarm deployed over a known, two-dimensional environment. The goal is to split a region of interest into a number of subregions such that the amount of effort required to cover each subregion is approximately equal with respect to an objective function. Towards this end, the problem is defined as a distributed optimization problem, where the decision varibles are the locations of robots. Then, the proposed solution approa...

Citation Formats

M. Ozay, I. Esnaola, F. T. Yarman Vural, S. R. Kulkarni, and H. V. Poor, “Distributed Models for Sparse Attack Construction and State Vector Estimation in the Smart Grid,” 2012, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54506.