In order to meet the lightweight requirements of high-speed trains, the inductancecapacitance (LC) resonance circuits are cancelled in the traction drive system of some high-speed electric multiple units (EMUs) in China, which will lead to large low-order current harmonics on the grid side in the traction drive system of EMUs, seriously affecting the power quality. Therefore, the low-order harmonic current of the traction drive system of an EMU is studied in this paper. Firstly, the working principle of a four-quadrant pulse rectifier in a traction drive system is analyzed, and then the generation mechanism of loworder current harmonics on the grid side is studied deeply. Secondly, the voltage outer loop and current inner loop control of a four-quadrant pulse rectifier are optimized respectively. In the voltage outer loop control, a Butterworth filter is designed to suppress the beat frequency voltage of the DC side voltage, so as to indirectly suppress the low-order current harmonics. In the current inner loop, a quasi-proportional resonance (PR) controller with harmonic compensation is used to suppress low-order current harmonics, and a novel loworder current harmonics suppression strategy based on the Butterworth filter and quasi-PR controller is proposed. Finally, the results of the simulated validation of the proposed control strategy show that compared with the existing method of the notch filter ¸ PR controller, the proposed optimal control strategy has a better effect on low-order current harmonic suppression, and improves the dynamic performance of the control system, further showing the correctness and effectiveness of the optimal control strategy.

Illegal elements use the characteristics of an anonymous network hidden service mechanism to build a dark network and conduct various illegal activities, which brings a serious challenge to network security. The existing anonymous traffic classification methods suffer from cumbersome feature selection and difficult feature information extraction, resulting in low accuracy of classification. To solve this problem, a classification method based on three-dimensional Markov images and output self-attention convolutional neural network is proposed. This method first divides and cleans anonymous traffic data packets according to sessions, then converts the cleaned traffic data into three-dimensional Markov images according to the transition probability matrix of bytes, and finally inputs the images to the output self-attention convolution neural network to train the model and perform classification. The experimental results show that the classification accuracy and F1-score of the proposed method for Tor, I2P, Freenet, and ZeroNet can exceed 98.5%, and the average classification accuracy and F1-score for 8 kinds of user behaviors of each type of anonymous traffic can reach 93.7%. The proposed method significantly improves the classification effect of anonymous traffic compared with the existing methods.