Monitoring, operation, and protection of distribution power grids fundamentally rely on the accurate estimation of line impedances. However, line impedance estimation is challenging due to the difficulties in modeling the dependence on line temperature and aging. This paper proposes a new data-driven strategy to estimate low-voltage (LV) and medium-voltage (MV) line impedances using an advanced metering infrastructure (AMI). In the proposed strategy, two-level optimization problems are formulated using generalized equations for voltage drops along LV and MV lines and then extended based on AMI data collected over time. Hierarchical estimation is achieved using the local and global references to the LV and MV root buses, respectively, enabling parallel estimation for individual LV grids and thus reduced computation time. Reinforcement learning is also integrated to compensate for possible measurement errors in the AMI data, ensuring robust estimation of LV and MV line impedances. The proposed strategy is tested on a three-phase unbalanced MV grid with multiple single-phase LV grids under various conditions characterized by measurement samples and errors. The results of case studies and sensitivity analyses confirm that the proposed strategy improves the accuracy and robustness of line impedance estimation at both LV and MV levels.

中文翻译：

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用于分层估计配电线路阻抗的 AMI 数据驱动策略

配电网的监测、运行和保护从根本上依赖于对线路阻抗的准确估计。然而，由于难以对线路温度和老化的依赖性进行建模，线路阻抗估计具有挑战性。本文提出了一种新的数据驱动策略，使用高级计量基础设施 (AMI) 来估算低压 (LV) 和中压 (MV) 线路阻抗。在所提出的策略中，两级优化问题是使用 LV 和 MV 线路电压降的广义方程制定的，然后根据随时间收集的 AMI 数据进行扩展。分别使用对 LV 和 MV 根母线的局部和全局参考实现分层估计，从而实现对各个 LV 电网的并行估计，从而减少计算时间。还集成了强化学习以补偿 AMI 数据中可能存在的测量误差，确保对 LV 和 MV 线路阻抗进行稳健估计。在以测量样本和误差为特征的各种条件下，在具有多个单相低压电网的三相不平衡中压电网上测试了所提出的策略。案例研究和敏感性分析的结果证实，所提出的策略提高了 LV 和 MV 水平的线路阻抗估计的准确性和鲁棒性。