  Time-synchronized state estimation is a challenge for distribution systems
because of limited real-time observability. This paper addresses this challenge
by formulating a deep learning (DL)-based approach to perform unbalanced
three-phase distribution system state estimation (DSSE). Initially, a
data-driven approach for judicious measurement selection to facilitate reliable
state estimation is provided. Then, a deep neural network (DNN) is trained to
perform DSSE for systems that are incompletely observed by synchrophasor
measurement devices (SMDs). Robustness of the proposed methodology is
demonstrated by considering realistic measurement error models for SMDs. A
comparative study of the DNN-based DSSE with classical linear state estimation
indicates that the DL-based approach gives better accuracy with a significantly
smaller number of SMDs.
