【摘要】 在配电网多源量测体系中，多源量测设备的采样频率和时间戳不同步，以及系统中的不良数据，都将导致量测数据间存在偏差，从而影响状态估计的准确性。为此，提出了一种多源量测数据下基于自适应扩展卡尔曼滤波（EKF）的动态状态估计方法。首先，针对多源量测数据的非同步问题，提出了一种基于动态时间规整（DTW）的多源数据时间戳对齐策略，实现量测数据的同步。其次，针对系统中的不良数据，提出了一种集成不良数据自适应检测和滤除环节的EKF状态估计方法，降低了不良数据对状态估计的影响。最后，在IEEE 33节点系统中进行算例测试，并与未考虑多源量测数据融合和异常值检测的传统EKF方法进行比较。结果表明，所提方法提高了估计结果的鲁棒性和可靠性。更多还原

【Abstract】 In the multi-source measurement system of distribution network,the sampling frequency and time stamp of multi-source measurement equipment are not synchronized,as well as the bad data in the system,which will lead to the bias among the measurement data,thus affecting the accuracy of state estimation. To this end,a dynamic state estimation method based on adaptive extended Kalman filtering(EKF) under multi-source measurement data was proposed. Firstly,to address the non-synchronization problem of multi-source measurement data,a multi-source data timestamp alignment strategy based on dynamic time warping(DTW)was proposed to realize the synchronization of measurement data. Secondly,for the bad data in the system,an EKF state estimation method integrating the bad data adaptive detection and filtering link was proposed to overcome the effect of bad data on state estimation. Finally,an arithmetic test was performed in an IEEE 33 node system and compared with a conventional EKF method that did not consider the fusion of multi-source metrology data and outlier detection. The results show that the proposed method improves the robustness and reliability of the estimation results.更多还原