False Data Injection Attacks on LFC Systems: An AI-Based Detection and Countermeasure Strategy

The development of integrated energy system has promoted the development of the power system into the largest and most complex cyber-physical energy system, where the change in the openness of the load frequency control (LFC) system has also increased its susceptibility to false data injection attacks (FDIAs). This paper porposes an artificial intelligence (AI) based detection and countermeasure strategy to protect LFC systems from FDIAs. First, The levenberg-marquarelt-back propagation (LM-BP) neural networks (NNs) is trained by collecting the historical data of frequency deviation, power deviation of contact line, and active power load deviation. Second, the output control signal of LM-BP NNs is tested against the output of the system controller for residuals to determine whether has FDIAs. Furthermore, to resist the negative effects of FDIAs, the control signal calculated by the LM-BP NNs will replace the traditional proportion integration differentiation (PID) output. Finally, The advantages of the proposed strategy are illustrated by the two interconnected power systems.

Automated summary

The development of integrated energy system has led to the emergence of the largest and most complex cyber-physical energy system. The openness of the load frequency control system has made it vulnerable to false data injection attacks. This paper presents an artificial intelligence-based strategy to detect and counteract these attacks, using LM-BP neural networks to analyze historical data and replace traditional control methods with LM-BP NNs to mitigate the effects of FDIAs, as demonstrated in interconnected power systems.