Robust Forecasting-aided State Estimation Method of Active Distribution Network Considering Small Sample Imbalance
To solve the problem of small sample imbalance in state estimation of active distribution networks(ADNs),this paper proposes a robust forecasting-aided state estimation(FASE)method based on the improved synthetic minority oversampling technique(SMOTE)and particle filter(PF)of Prophet.The method enables state estimation of ADNs.Firstly,to handle the small-sample imbalance problem,a hash function is constructed based on the data features of the ADN and an optimization approach is proposed using the hash function for the Borderline-SMOTE+Tomek-Links algo-rithm.Secondly,considering the large amount of data and the stochastic output of distributed energy resources in ADNs,the Prophet prediction model is used for state estimation of ADNs,and a robust FASE method based on Prophet-PF is proposed for fast and accurate estimation of ADNs states.Finally,numerical simulations are conducted on standard IEEE 118-bus distribution network and a DTU 7k 47 distribution system to evaluate the proposed method.The results demon-strate that the proposed method has high accuracy and robustness,providing useful references for state estimation in ADNs.
active distribution networkforecasting-aided state estimationsynthetic minority oversampling techniqueHash functionProphetparticle filter
万方数据
维普
