受负荷随机变化、需求响应参与、分布式电源波动、量测装置种类多等因素的影响，容易出现配电网量测数据值异常，导致配电网动态状态估计精度下降。为了提高配电网状态估计的精度，提出了一种基于自适应H_∞容积卡尔曼滤波的配电网动态状态估计方法。首先，在容积卡尔曼滤波基础上，将自适应因子和H_∞滤波器相结合，对模型误差问题进行处理与限制。其次，结合噪声估值器，对过程噪声中的参数进行在线估计，减少噪声对预测误差的影响。最后，对典型配电网69节点系统进行仿真，仿真结果表明：该方法在系统正常运行、需求响应参与削峰填谷以及负荷发生突变这3种场景下，其估计精度均提高10%以上，保持了相对高的估计精度。

Due to the random variation of load, the participation of demand response, the fluctuation of distributed power supply, and the variety of measurement devices, the measurement data of distribution network is prone to abnormal values, which leads to the decline of dynamic state estimation accuracy. In order to improve the accuracy of distribution network state estimation, this paper proposed a dynamic state estimation method for distribution network based on adaptive H_∞ cubature Kalman filter. Firstly, based on the cubature Kalman filter, the adaptive factor and H_∞ filter were combined to deal with and limit the model error. Secondly, combined with the noise estimator, the parameters in the process noise were estimated online to reduce the influence of noise on the prediction error. Finally, a typical distribution network system with 69 nodes was simulated. The simulation results show that the estimation accuracy of the proposed method is improved by more than 10% under three scenarios: system normal operation, demand response participating in peak load shaving and load mutation, maintaining a relatively high estimation accuracy.