Decentralized robust state estimation for hybrid AC/DC distribution systems with smart meters. (March 2022) - Huang, Manyun; Zhao, Junbo; Wei, Zhinong; Pau, Marco; Sun, Guoqiang
Highlights: The proposed decentralized robust state estimation (DRSE) technically integrates multi-source data with different timescales for hybrid AC/DC distribution system state estimation. A slow timescale smart meters aided DNN model is developed to extract hidden system statistical information and allow deriving nodal power injections that guarantee the network observability. The proposed DRSE is executed in a decentralized manner with the detailed converter model for achieving efficient SE and accommodating flexible operating modes. A linear WLAV-based formulation for AC and DC regions is derived that achieves higher computational efficiency and offers robustness of suppressing bad data automatically. Abstract: Hybrid AC/DC distribution systems are becoming a popular means to accommodate the increasing penetration of distributed energy resources and flexible loads. This paper proposes a decentralized robust state estimation (DRSE) method for hybrid AC/DC distribution systems using multiple sources of data. In the proposed decentralized implementation framework, a unified robust linear state estimation model is derived for each AC and DC regions, where the regions are connected via AC/DC converters and only limited information exchange is needed. In this context, estimation accuracy may be suffering due to linearization. To enhance the estimation accuracy, a deep neural network (DNN) based on the smart meter data is used to extract hidden system statistical information and allow deriving nodal power injections that keep up with the real-time measurement update rate. This provides the way of integrating smart meter data, SCADA measurements and zero injections together for state estimation. Simulations on two hybrid AC/DC distribution systems show that the proposed DRSE has only slight accuracy loss by the linearization formulation but offers robustness of suppressing bad data automatically, as well as benefits of improving computational efficiency.