A Novel Detection Algorithm to Identify False Data Injection Attacks on Power System State Estimation

Mehdi Ganjkhani, Seyedeh Narjes Fallah, Sobhan Badakhshan, Shahaboddin Shamshirband and Kwok-wing Chau
Additional contact information
Mehdi Ganjkhani: Department of Electrical Engineering, Sharif University of Technology, Tehran P.O. Box 11365-11155, Iran
Seyedeh Narjes Fallah: Independent Researcher, Sari 4816783787, Iran
Sobhan Badakhshan: Department of Electrical Engineering, Sharif University of Technology, Tehran P.O. Box 11365-11155, Iran
Shahaboddin Shamshirband: Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Kwok-wing Chau: Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China
Authors registered in the RePEc Author Service: Shahab S Band

Energies, 2019, vol. 12, issue 11, 1-19

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: state estimation; false data injection attack (FDIA); artificial neural network (ANN); nonlinear autoregressive exogenous (NARX) bad data detection (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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