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

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Time-synchronized state estimation for reconfigurable distribution networks is challenging because of limited real-time observability. This article 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.

Original languageEnglish (US)
Article number9003514
JournalIEEE Transactions on Instrumentation and Measurement
StatePublished - 2022


  • Deep neural network (DNN)
  • state estimation
  • synchrophasor measurements
  • topology identification (TI)

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering


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