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Time-synchronized state estimation is a challenge for distribution systemsbecause of limited real-time observability. This paper addresses this challengeby formulating a deep learning (DL)-based approach to perform unbalancedthree-phase distribution system state estimation (DSSE). Initially, adata-driven approach for judicious measurement selection to facilitate reliablestate estimation is provided. Then, a deep neural network (DNN) is trained toperform DSSE for systems that are incompletely observed by synchrophasormeasurement devices (SMDs). Robustness of the proposed methodology isdemonstrated by considering realistic measurement error models for SMDs. Acomparative study of the DNN-based DSSE with classical linear state estimationindicates that the DL-based approach gives better accuracy with a significantlysmaller number of SMDs.

Time Synchronized State Estimation for Incompletely Observed Distribution Systems Using Deep Learning Considering Realistic Measurement Noise