This study develops a multi-objective optimization framework for high-speed train integrated grounding systems, addressing the conflicting goals of minimizing train body (TB) current and transient overvoltage. Using a validated catenary–train–rail coupling model and the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the study identifies Pareto-optimal grounding configurations that reduce TB current and overvoltage by up to 70% and 39%, respectively. The proposed framework eliminates manual tuning limitations and provides a systematic design tool for safer and more efficient grounding systems.

This paper has developed a high-fidelity ‘rail-train’ coupling model, which was rigorously validated against experimental data, demonstrating its accuracy in simulating both TB current under dynamic conditions and high-frequency overvoltage during transient events like pantograph raising and VCB switching. This model then served as the foundation for the optimization process. The paper ended with addressing the critical design challenge of balancing train body (TB) current and transient overvoltage in the integrated grounding system of high-speed trains. Moving beyond conventional single-objective or weighted-sum optimization approaches. The problem was formulated and solved using a robust multi-objective framework centered on Non-dominated Sorting Genetic Algorithm II.

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