针对谐振接地系统发生单相接地故障时，故障信号暂态特征易受噪声干扰造成馈线识别误判问题，提出了一种参数优化变分模态分解（variational mode decomposition, VMD）和改进D-S证据理论的故障特征融合的选线方法。首先，对于VMD的惩罚因子Alpha和分解层数K参数难以选择问题，引入牛顿拉夫逊优化算法（Newton-Raphson-based optimizer, NRBO），完成不同噪声环境下alpha和K参数自适应确定。接着，融合峭度、极性、暂态能量3种故障特征，并在D-S证据理论加入Jousselme距离，以避免噪声影响故障特征造成相悖的结果，得到各馈线发生故障的概率，以此确定故障馈线。最后，Simulink仿真结果表明，在不同强度噪声环境和不同故障情形下均能正确识别故障馈线。相比其他参数优化算法具有更快的收敛速度，且引入Jousselme距离进一步提高故障馈线识别的可靠性。

To address the issue of fault signal transient characteristics being easily affected by noise, leading to misidentification of feeders in single-phase ground faults within resonant grounded systems, a line selection method was proposed that combines parameter-optimized VMD (variational mode decomposition) and improved D-S (dempster-shafer) evidence theory for fault feature fusion. First, to tackle the challenge of selecting the penalty factor Alpha and decomposition level K parameters in VMD, NRBO (Newton-Raphson-based optimizer) is introduced to adaptively determine these parameters under different noise environments. Next, three fault features—kurtosis, polarity, and transient energy—was fused, and the Jousselme distance was incorporated into D-S evidence theory to prevent conflicting results caused by noise interference on fault features. This approach provides the probability of fault occurrence on each feeder, allowing for accurate fault feeder identification. Finally, Simulink simulation results demonstrate that the method can accurately identify the fault feeder across various noise levels and fault scenarios. Compared to other parameter optimization algorithms, it achieves faster convergence, and the introduction of Jousselme distance further enhances the reliability of fault feeder identification.