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arXiv · 2104.07208

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

Behrouz Azimian·Reetam Sen Biswas·Shiva Moshtagh·Anamitra Pal·Lang Tong·Gautam Dasarathy
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Abstract

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 for time-synchronized DNN-based TI and DSSE, respectively, for systems that are incompletely observed by synchrophasor measurement devices (SMDs) in real-time. A data-driven approach for judicious SMD placement to facilitate reliable TI and DSSE is also provided. Robustness of the proposed methodology is demonstrated by considering non-Gaussian noise in the SMD measurements. A comparison of the DNN-based DSSE with more conventional approaches indicates that the DL-based approach gives better accuracy with smaller number of SMDs.

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Behrouz Azimian, Reetam Sen Biswas, Shiva Moshtagh, Anamitra Pal, Lang Tong, Gautam Dasarathy. 2021-04-15. State and Topology Estimation for Unobservable Distribution Systems using Deep Neural Networks. https://arxiv.org/abs/2104.07208

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