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.

Aiming to address power consumption issues of various equipment in metro stations and the inefficiency of peak shaving and valley filling in the power supply system, this study presents an economic optimization scheduling method for the multi-modal “source-network-load-storage” system in metro stations. The proposed method, called the Improved Gray Wolf Optimization Algorithm (IGWO), utilizes objective evaluation criteria to achieve economic optimization. First, construct a mathematical model of the “sourcenetwork- load-storage” joint system with the metro station at its core. This model should consider the electricity consumption within the station. Secondly, a two-layer optimal scheduling model is established, with the upper model aiming to optimize peak elimination and valley filling, and the lower model aiming to minimize electricity consumption costs within a scheduling cycle. Finally, this paper introduces the IGWO optimization approach, which utilizes meta-models and the Improved Gray Wolf Optimization Algorithm to address the nonlinearity and computational complexity of the two-layer model. The analysis shows that the proposed model and algorithm can improve the solution speed and minimize the cost of electricity used by about 5.5% to 8.7% on the one hand, and on the other hand, it improves the solution accuracy, and at the same time effectively realizes the peak shaving and valley filling, which provides a proof of the effectiveness and feasibility of the new method.