Critical Meter Identification and Network Embedding Based Attack Detection for Power Systems Against False Data Injection Attacks
Modern power systems are becoming vulnerable to false data injection attacks (FDIAs) due to the high penetration of communication components. This research develops a multi-task framework for power systems against FDIAs, which aims to: 1) study the mechanism for the response model of grids to FDIAs; 2) identify and protect critical meter measurements to reduce the loss of grids to the attacks; 3) detect attacks which may lead to severe consequences. To this end, a quantitative critical meter index ( CMI ) and a severe attack detection method based on network embeddings are proposed. The performance of the proposed method is evaluated by FDIAs simulations in IEEE 30- and 118-bus systems. Results show that protecting the identified meters can reduce the load shedding of 118-bus system greatly, and the accuracy rate of the proposed method to detecte severe attacks reaches 96.62% and 98.91% for 30- and 118-bus systems against FDIAs, respectively
Year of publication: |
[2022]
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Authors: | Lian, Xianglong ; Qian, Tong ; Zhang, Yin ; Tang, W.H ; Wu, Q.H |
Publisher: |
[S.l.] : SSRN |
Saved in:
freely available
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