Fault-tolerant Integration of Charge and Discharge Information Security for Multi-Scale Time Series Models

Abstract

In this paper, the power mode of winding and converter topology are integrated. Considering the filter characteristics and electromagnetic torque control of the system during charging/discharging, the integrated structures of one-phase full bridge, three-phase full bridge and three-phase four-arm bridge are studied. The single-phase all-bridge integrated structure can completely suppress the electromagnetic torque generated by the motor during charging and discharging, and make the filtering effect of the system better. In this project, a multi-scale full convolutional deep neural network method based on time series is studied. The multi-scale full convolution time series model is used for training, and the future continuous signals can be monitored according to the trained model based on the power signal data collected from high-voltage power lines. Experiments show that the proposed method can effectively extract the required data.