False Data Injection Information Detection Method for Power Systems Based on Improved Random Forest

With the widespread application of information technology and the intelligence of the power grid, more refined and efficient elements have been injected into the power system, but at the same time, it has also brought new network security threats to the power system. In order to maintain the stable operation of the system, reduce the probability of power accidents, and reduce and avoid losses caused by network attacks, how to effectively detect data attacks in smart grids has become a research hotspot in the field of power system security. We emphasized the necessity of system attack detection and defense, and conducted in-depth research on the current application status of various attack detection technologies. We first model the load frequency control system, laying the foundation for subsequent system attack detection. Subsequently, through training and comparative analysis of the application of the random forest algorithm in detecting false data injection attacks, it was found that the algorithm has superiority in classical performance indicators, which also proves its advantages in good detection timeliness, high detection efficiency, and wide applicability. Finally, the effectiveness of the study was verified in practical application scenarios through simulation analysis.