Sensor Scheduling-Based Detection of False Data Injection Attacks in Power System State Estimation

2021 ◽  
Author(s):  
Sho OBATA ◽  
Koichi KOBAYASHI ◽  
Yuh YAMASHITA
Keyword(s):  
Power System ◽  
State Estimation ◽  
Sensor Scheduling ◽  
System State ◽  
Power System State Estimation ◽  
False Data Injection ◽  
Injection Attacks

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

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2209 ◽  
Author(s):  
Mehdi Ganjkhani ◽  
Seyedeh Narjes Fallah ◽  
Sobhan Badakhshan ◽  
Shahaboddin Shamshirband ◽  
Kwok-wing Chau
Keyword(s):  
Neural Network ◽  
Power System ◽  
State Estimation ◽  
Data Detection ◽  
System State ◽  
Power System State Estimation ◽  
Bad Data Detection ◽  
False Data Injection ◽  
Injection Attacks ◽  
Bad Data

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.

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