  Time-synchronized state estimation for reconfigurable distribution networks
is challenging because of limited real-time observability. This paper addresses
this challenge by formulating a deep learning (DL)-based approach for topology
identification (TI) and unbalanced three-phase distribution system state
estimation (DSSE). Two deep neural networks (DNNs) are trained to operate in a
sequential manner for implementing DNN-based TI and DSSE for systems that are
incompletely observed by synchrophasor measurement devices (SMDs). A
data-driven approach for judicious measurement selection to facilitate reliable
TI and DSSE is also provided. Robustness of the proposed methodology is
demonstrated by considering realistic measurement error models for SMDs as well
as presence of renewable energy. A comparative study of the DNN-based DSSE with
classical linear state estimation (LSE) indicates that the DL-based approach
gives better accuracy with a significantly smaller number of SMDs
