Research on location detection method of power network false data injection based on FCN-GRU

Abstract

In order to realize the position detection of false data injection, a position detection model based on bi-directional gated cycle unit optimized full convolution neural network is proposed. In view of the problem that the traditional false data injection detection is limited to judging whether the attack occurs or not, the processing of multi-level detection is proposed to realize the fine discrimination of the attacked data and effectively avoid the resource loss caused by direct location detection. According to the time series characteristics of the measured data, the bi-directional gated cycle unit is applied to the convolution layers of the full convolution neural network, which can effectively extract the spatiotemporal features between the data and improve the accuracy and efficiency of false data injection detection. The experimental results show that the optimized position detection model has a certain improvement in detection accuracy and efficiency compared with the traditional convolution neural network.