Tongxu Zhang

Mr. Tongxu Zhang


Research Focus & Metrics

Academic Career Length

2Years (2025-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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11 November 2025 in Sustainability
Verified
Journal Article
Open access
PDF file
PPPeng Peng
Peng Peng
TZTongxu Zhang
Tongxu Zhang
XYXiangyan Yang
Xiangyan Yang
YCYaozhen Chen
Yaozhen Chen
GCGuotao Cao
Guotao Cao
Mingli WuMingli Wu
Mingli Wu
The integration of electric railways with renewable energy sources is crucial for advancing sustainable transportation and building clean, low-carbon, and efficient energy systems in alignment with global sustainable development goals. However, the application of photovoltaic (PV) integration into railway traction power supply systems may exacerbate resonance phenomena between electric locomotives and the traction network. It is therefore necessary to study the impedance frequency characteristics (IFCs) of traction networks to minimize harmonic resonance overvoltage. In this paper, a harmonic impedance model of the sustainable traction power supply system (STPSS) is established, and an impedance analysis method is adopted to reveal the influence law of grid-connected PV inverters on the IFCs of STPSSs. Additionally, to improve the stability of STPSSs, a multi-parameter co-tuning method based on an improved particle swarm optimization algorithm is proposed. This method constructs a multi-objective function that includes resonance frequency, impedance magnitude, and filtering cost, thereby realizing the automatic optimization of the control parameters and filtering parameters of PV inverters. The results demonstrate a 56% reduction in the maximum impedance magnitude within the 0–5 kHz frequency range and a 10.8% cost reduction in the LCL filter implementation, confirming the effectiveness of the proposed optimization model. Results show that the maximum impedance magnitude of the optimized system in the frequency range of 0–5 kHz can be reduced by 56%. Moreover, the cost of LCL filters can be reduced by 10.8% through component value optimization. These findings validate the effectiveness of the proposed method.