This paper introduces a robust sparse recovery model for compressing bad data and state estimation(SE),based on a revised multi-stage convex relaxation(R-Capped-L1)model.To improve the calculation efficiency,a fast de...This paper introduces a robust sparse recovery model for compressing bad data and state estimation(SE),based on a revised multi-stage convex relaxation(R-Capped-L1)model.To improve the calculation efficiency,a fast decoupled solution is adopted.The proposed method can be used for three-phase unbalanced distribution networks with both phasor measurement unit and remote terminal unit measurements.The robustness and the computational efficiency of the R-Capped-Ll model with fast decoupled solution are compared with some popular SE methods by numerical tests on several three-phase distribution networks.展开更多
基金supported in part by the National Key Research and Development Plan of China(No.2018YFB0904200)in part by the National Natural Science Foundation of China(No.51725703).
文摘This paper introduces a robust sparse recovery model for compressing bad data and state estimation(SE),based on a revised multi-stage convex relaxation(R-Capped-L1)model.To improve the calculation efficiency,a fast decoupled solution is adopted.The proposed method can be used for three-phase unbalanced distribution networks with both phasor measurement unit and remote terminal unit measurements.The robustness and the computational efficiency of the R-Capped-Ll model with fast decoupled solution are compared with some popular SE methods by numerical tests on several three-phase distribution networks.
