在新型电力系统背景下基于量测数据的参数辨识与修正是配电网数字化与智能化的关键需求。为解决线路和变压器参数的在线辨识与修正问题，提出了一种融合配电网数学模型与台区变压器低压侧量测数据的参数辨识方法。首先，通过解析CIM文件获取拓扑结构，并依据资产数据设定元件初始参数；进而采用拟牛顿-拉夫逊法，基于多组量测数据构建雅可比矩阵，通过求解欠定或超定方程实现参数迭代更新。最后，利用辨识参数重新计算低压侧电压，并与实测值对比验证。华南某实际配电网数据的实验结果表明，随数据组数与迭代次数增加，平均相对误差趋于稳定，证明了所提方法具有良好的准确性与实用性，可为配电网潮流计算、状态估计及数字孪生构建提供有效支撑。

Under the background of the new power system, parameter identification and correction based on measurement data are key requirements for the digitalization and intelligence of distribution networks. A parameter identification method is proposed that integrates the mathematical model of the distribution network with measurement data collected from the low-voltage side of distribution transformers, aiming to address the online identification and correction of line and transformer parameters. Firstly, the topology is obtained by parsing CIM files, and the initial parameters of components are set according to asset data. Then, a quasi-Newton-Raphson method is employed to construct the Jacobian matrix based on multiple sets of measurement data, enabling iterative parameters updated by solving underdetermined or overdetermined equations. Finally, the identified parameters are used to recalculate the low-voltage side voltages, which are then compared with the measured values for validation. Experimental results based on data from a practical distribution network in South China demonstrate that, with increasing data sets and iteration times, the average relative errors converge to a stable level. This verifies that the proposed method achieves high accuracy and practicality, and can provide effective support for power flow calculation, state estimation, and digital twin construction in distribution networks.