A Detection Mechanism for PMU Data Manipulation Attacks by Using Sliced Recurrent Convolutional Attention Module

Background: Phasor Measurement Unit (PMU) Data Manipulation Attacks (PDMA) can change the state estimates of power systems and cause significant damage to the smart grid. So it is vital to research a method to detect it. Objective: In this paper, we propose a detection mechanism and model for PDMA. Method: Firstly, the distribution's characteristics of Phasor Data Concentrator (PDC) and PMU are analyzed, and we use these characteristics to detect a PDMA detection mechanism that could help us reduce the number of detection samples. Secondly, we use the Sliced Recurrent Neural Network (SRNN) to extract the time series data's temporal characteristics of PMU data. Thirdly, based on the temporal characteristics, the Convolutional Neural Networks (CNN) and Attention mechanisms are used to extract the spatial features of these data. Finally, we sent the spatial features to the Fully Layer and used the softmax function to classify. Results: The proposed SRCAM in this paper has two advantages. One is that it implements the parallel computation on data by using the segmentation concept of SRNN, which reduces the computation time. The other is that using the Attention mechanism on CNN can make the spatial features more prominent. At the end of the paper, we do many comparative experiments between SRCAM and other models, such as some algorithms of Machine learning and soft computing. We take IEEE node data as experimental data and TensorFlow as an experimental platform. Experimental results show that the SRCAM model has an excellent performance of the detection of PDMA with high precision and accuracy. Conclusion: The superiority of SRCAM is theoretically and experimentally proved in this paper. As we expected, SRCAM showed great results in the application of PDMA detection.