Risk Forecast Assessment of Transmission Line Capacity Increase Under Cyberattacks

In the process of a dynamic capacity increase of transmission lines, to prevent false data injection attacks, the stability and adjustability of transmission network capacity increase is damaged. Therefore, this study studies a risk probability forecast assessment model to improve the robustness of the model under false data injection attacks. It is important to ensure reliability and security after capacity increase operation. This novel method first probabilistically models the uncertainty of meteorological data present in risk assessment. In addition, the conductor current prediction algorithm is introduced to make the risk assessment model somewhat predictive. Finally, the conductor temperature is analyzed using Monte Carlo simulations to complete the risk assessment of instability. The simulation results show that the proposed probabilistic prediction model enables effective conductor heating risk prediction in dynamic capacity increase even in a severe cyberattacks environment.