多源量测数据下基于自适应EKF的动态状态估计方法OA

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.