State and Topology Estimation for Unobservable Distribution Systems using Deep Neural Networks
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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
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