基于wk-GDNN模型的虚假数据注入攻击检测研究OA

The false data injection attack(FDIA)has a significant impact on the security of the power grid system.Currently,deep learning still has shortcomings in dealing with data processing of power grid topology structure information and capturing long-term dependency relationships.To further improve the accuracy and robustness of the current detection model for false data injection attacks in smart grids,this paper introduces the Wiener-Kinchin(wk)theorem to process the data in frequency domain,and innovatively proposes a graph frequency domain convolutional neural network detection model based on Decoder optimization(wk GDNN,Wiener-Khinchin guided dual-domain neural network).The wk-GDNN model converts the time feature information hidden in the data into frequency domain information.Secondly,it combines the power grid topology perception ability of GCN,and optimizes the spatiotemporal feature extraction through the context information extraction ability of decoder,which improves the detection accuracy and verifies the effectiveness based on IEEE-14/118 node system simulation.The experimental results showed that the F1 scores of the model were 0.9798 and 0.9761,respectively,with an average improvement of 6.67%compared to the comparison model.The results indicate that the frequency domain preprocessing based on the wk theorem and the subsequent frequency domain graph convolution co decoding provide a new paradigm for joint modeling of FDIA detection at multiple scales from time domain to frequency domain,and from nodes to systems.