An Ensembled ELMs Based Defense Mechanism Against Cyber Attack on Power Systems

Abstract

State estimation is critical for the normal operation of power system. However, due to the aging of electric infrastructures, the power grid become more vulnerable to cyber-attack. Therefore, in this paper, scenario-based two-stage sparse cyber-attack models for smart grid with complete and incomplete network information are proposed. In previous traditional state estimator, bad data detector (BDD) is used to prevent power systems from the smearing effect of the unsteady power system caused by the bad data. However, the researches have pointed out that BDD can’t recognize the abnormal state which launch by the hackers who know the topologies of the power system. Then, in order to detect false data effectively, we proposed a new detect mechanism based on extreme learning machine (ELM). In the proposed defense mechanism, we combine numbers of ELM whit certain rules, which objective is to improve the precision of the detection. Finally, the effectiveness and validation of the proposed method is verified on standard IEEE14-bus AIEEE57-bus system.