【目的】由于量测设备不足，配电网的可观测性较差，影响了状态估计的准确性。为此，文章提出基于动态分区的弱可观配电网先验信息增强状态估计，利用可观区域量测实现全节点状态估计。【方法】首先，依据量测数据采集情况对节点进行可观性分析与实时动态分区，并在此基础上构建区域状态映射模型，从而实现不可观区域状态的实时估计。然后，基于系统历史状态的先验状态信息设置预测误差协方差矩阵，建立先验信息增强的扩展卡尔曼滤波（prior-enhanced extended Kalman filter，PEEKF）模型，避免后验估计中对预测误差协方差的更新，提高估计效率的同时获取系统可观区域的精确状态。最后，通过状态映射模型将可观区域状态映射得到不可观区域状态，获取配电网不可观区域的状态估计值。【结果】在IEEE 33节点和IEEE 95节点系统进行仿真测试，算法在2个系统中的平均绝对百分比误差稳定在0.4%以下，与扩展卡尔曼滤波、无迹卡尔曼滤波及自适应插值卡尔曼滤波相比，估计精度显著提升。【结论】文章所提方法能有效进行动态分区和状态映射，实现弱可观配电网高实时、高精度状态跟踪。

[Objective] Due to insufficient measurement devices, the observability of distribution networks is relatively poor, which undermines the accuracy of state estimation. To address this issue, this paper proposes a prior-enhanced state estimation method for weakly observable distribution networks based on dynamic partitioning, which enables full-node state estimation utilizing measurements from observable areas. [Methods] First, node observability analysis and real-time dynamic partitioning are performed in accordance with the available measurement data. On this basis, a regional state mapping model is constructed to realize real-time state estimation in unobservable areas. Second, leveraging the prior state information from historical system states, a prediction error covariance matrix is formulated to establish a prior-enhanced extended Kalman filter (PEEKF) model. This method avoids updating the error covariance during posterior estimation, thereby improving estimation efficiency while maintaining accurate estimation states in the observable areas. Finally, the states of observable areas are mapped to the states of unobservable areas through the state mapping model, yielding the state estimates for the unobservable areas of the distribution networks. [Results] Simulation tests are conducted on the IEEE 33-node and 95-node systems. The average absolute percentage error of the algorithm remains consistently below 0.4% in both systems. Compared with the Extended Kalman Filter, Unscented Kalman Filter, and Adaptive Interpolation Kalman Filter, the proposed method achieves significantly higher estimation accuracy. [Conclusions] The proposed method can effectively perform dynamic partitioning and state mapping, and achieve high real-time and high-precision state tracking of weakly observable distribution networks.