Real-time power system state estimation via deep unrolled neural networks

Liang Zhang, Gang Wang, Georgios B. Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Contemporary smart power grids are being challenged by rapid voltage fluctuations, due to large-scale deployment of electric vehicles, demand response programs, and renewable generation. To secure grid operations, it becomes increasingly critical to obtain accurate estimates of power system AC states, namely the complex voltages at all buses, in real time. With the emergent nonconvexity however, past optimization based power system state estimation (PSSE) schemes are either sensitive to initialization, or computationally expensive. To bypass these hurdles, this paper advocates deep neural networks (DNNs) for PSSE. Different from model-agnostic NNs, that are difficult to tune and train, the novel model-specific DNN is obtained by unrolling state-of-the-art physics-based prox-linear PSSE solvers. The proposed prox-linear net requires a minimal tuning effort, and is easy to implement. Simulated tests show improved performance of the proposed prox-linear net relative to 'plain-vanilla' NN-, and the 'workhorse' Gauss-Newton-based PSSE solvers.

Original languageEnglish (US)
Title of host publication2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages907-911
Number of pages5
ISBN (Electronic)9781728112954
DOIs
StatePublished - Feb 20 2019
Event2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Anaheim, United States
Duration: Nov 26 2018Nov 29 2018

Publication series

Name2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings

Conference

Conference2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018
CountryUnited States
CityAnaheim
Period11/26/1811/29/18

Bibliographical note

Funding Information:
This work was supported in part by NSF grants 1508993, 1509040, and 1711471.

Keywords

  • Deep neural networks
  • Least-absolute-value
  • Power system state estimation
  • Prox-linear algorithm

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