电气传动2026，Vol.56Issue(1)：48-56,88,10.DOI:10.19457/j.1001-2095.dqcd26346
多源量测数据下基于自适应EKF的动态状态估计方法
Adaptive EKF-based Dynamic State Estimation Method Under Multi-source Measurement Data
摘要
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.关键词
配电网/状态估计/多源数据融合/不良数据检测/卡尔曼滤波Key words
distribution network/state estimation/multi-source data fusion/bad data detection/Kalman filtering(KF)分类
信息技术与安全科学引用本文复制引用
戚振彪,鲍玉莹,范申,潘敏,胡朋飞,吴红斌..多源量测数据下基于自适应EKF的动态状态估计方法[J].电气传动,2026,56(1):48-56,88,10.基金项目
国网安徽省电力有限公司科技项目(521209240004) （521209240004）
