AI 中文总结
针对双侧供电铁路系统运行中因车辆频繁切换运行点致系统不稳定及振荡过电压问题，提出用可解释残差前馈神经网络结合SHapley加法解释训练车辆阻抗模型，还给出推导阻抗矩阵方法，经实际数据验证可准确评估系统高低频不稳定问题。
AI 中文摘要
双侧供电铁路系统增加了车辆在电网上的同时运行，可能导致系统不稳定和振荡过电压问题。由于车辆在运行过程中频繁切换运行点，因此在广泛的条件下分析系统稳定性至关重要。准确识别车辆变流器在多个运行点的黑箱阻抗对于研究铁路车辆-电网系统稳定性至关重要。然而，传统的阻抗识别方法需要大量数据且缺乏可解释性，导致巨大的计算和数据负担。本研究引入了一种可解释的残差前馈神经网络（ResFNN）并结合SHapley加法解释来训练车辆阻抗模型，在保持准确性的同时减少数据需求。此外，还提出了一种组件连接方法来推导双侧供电模式下多车辆铁路系统的阻抗矩阵。该方法考虑了车辆的动态移动性及其位置分布，并利用ResFNN识别阻抗进行稳定性分析。以实际铁路线路的实际运行数据为案例分析双侧供电铁路系统的稳定性。结果表明，该方法能够准确评估低频和高频不稳定问题。
英文摘要
The double-sided power supply railway system increases the simultaneous operation of vehicles on the grid, potentially causing system instability and oscillation overvoltage issues. As vehicles frequently switch operating points during operation, it is essential to analyze system stability across a wide range of conditions. Therefore, accurately identifying the black-box impedance of vehicle converters at multiple operating points is crucial for studying railway vehicle-grid system stability. However, traditional impedance identification methods require extensive data and lack interpretability, leading to significant computational and data burdens. This study introduces an interpretable residual feedforward neural network (ResFNN) combined with SHapley Additive exPlanations for training vehicle impedance models, reducing data requirements while maintaining accuracy. Additionally, a component connection method is proposed for deriving the impedance matrix of a multivehicle railway system under the double-sided feeding mode. This method incorporates the dynamic mobility of vehicles and their positional distribution, and it utilizes the ResFNN to identify impedance for stability analysis. Real operational data from actual railway lines is used as case study to analyze the stability of the double-sided power supply railway system. The results demonstrate that this approach accurately assesses both lowfrequency and high-frequency instability issues.
Comments25 pages. Accepted manuscript
Journal refIEEE Transactions on Transportation Electrification, pp. 1-1, 2024
