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Title: A novel detection algorithm to identify false data injection attacks on power system state estimation
Authors: Ganjkhani, M
Fallah, SN
Badakhshan, S
Shamshirband, S
Chau, KW 
Issue Date: 2019
Source: Energies, 2019, v. 12, no. 11, 2209
Abstract: This paper provides a novel bad data detection processor to identify false data injection attacks (FDIAs) on the power system state estimation. The attackers are able to alter the result of the state estimation virtually intending to change the result of the state estimation without being detected by the bad data processors. However, using a specific configuration of an artificial neural network (ANN), named nonlinear autoregressive exogenous (NARX), can help to identify the injected bad data in state estimation. Considering the high correlation between power system measurements as well as state variables, the proposed neural network-based approach is feasible to detect any potential FDIAs. Two different strategies of FDIAs have been simulated in power system state estimation using IEEE standard 14-bus test system for evaluating the performance of the proposed method. The results indicate that the proposed bad data detection processor is able to detect the false injected data launched into the system accurately.
Keywords: Artificial neural network (ANN)
False data injection attack (FDIA)
Nonlinear autoregressive exogenous (NARX) bad data detection
State estimation
Publisher: Molecular Diversity Preservation International (MDPI)
Journal: Energies 
EISSN: 1996-1073
DOI: 10.3390/en12112209
Rights: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
The following publication Ganjkhani M, Fallah SN, Badakhshan S, Shamshirband S, Chau K-W. A Novel Detection Algorithm to Identify False Data Injection Attacks on Power System State Estimation. Energies. 2019; 12(11):2209 is available at
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