Abstract:

When detecting false data injection attacks, if the detection accuracy of the detection model is poor, it will directly affect the detection effect of false data injection attacks. In order to effectively improve the detection accuracy of the detection model, the incremental detection of false data injection attacks based on the grey wolf algorithm is proposed to optimize the kernel extreme learning machine. The state of power system is estimated, and the attack behavior of false data injection is analyzed. On this basis, an incremental detection model of false data injection attack is established based on kernel extreme learning machine, and the model is optimized by grey wolf algorithm. Finally, the normalized results of the collected power system state data are used as the model input data, and the accurate detection of false data injection attacks in power system under incremental changes is realized through the optimized model. The experimental results show that using this method to detect false data injection attacks can get better detection effect and high precision results.

Key words: nuclear extreme learning machine, grey wolf optimization algorithm, electric power system, false data injection attack, optimization treatment