Power system state estimation using gauss-newton unrolled neural networks with trainable priors

Qiuling Yang, Alireza Sadeghi, Gang Wang, Georgios B. Giannakis, Jian Sun

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

Abstract

Power system state estimation (PSSE) aims at finding the voltage magnitudes and angles at all generation and load buses, using meter readings and other available information. PSSE is often formulated as a nonconvex and nonlinear least-squares (NLS) cost function, which is traditionally solved by the Gauss-Newton method. However, Gauss-Newton iterations for minimizing nonconvex problems are sensitive to the initialization, and they can diverge. In this context, we advocate a deep neural network (DNN) based "trainable regularizer"to incorporate prior information for accurate and reliable state estimation. The resulting regularized NLS does not admit a neat closed form solution. To handle this, a novel end-to-end DNN is constructed subsequently by unrolling a Gauss-Newton-type solver which alternates between least-squares loss and the regularization term. Our DNN architecture can further offer a suite of advantages, e.g., accommodating network topology via graph neural networks based prior. Numerical tests using real load data on the IEEE 118-bus benchmark system showcase the improved estimation performance of the proposed scheme compared with state-of-the-art alternatives. Interestingly, our results suggest that a simple feed forward network based prior implicitly exploits the topology information hidden in data.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728161273
DOIs
StatePublished - Nov 11 2020
Event2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020 - Tempe, United States
Duration: Nov 11 2020Nov 13 2020

Publication series

Name2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020

Conference

Conference2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020
Country/TerritoryUnited States
CityTempe
Period11/11/2011/13/20

Bibliographical note

Funding Information:
The work of Q. Yang, G. Wang, and J. Sun was supported in part by the National Natural Science Foundation of China under Grants 61522303, 61720106011, 61621063, and U1613225. Q. Yang was also supported by the China Scholarship Council. The work of A. Sadeghi and G. B. Giannakis was supported in part by the National Science Foundation under Grants 1711471 and 1901134.

Publisher Copyright:
© 2020 IEEE.

Keywords

  • Gauss-Newton unrolled neural networks
  • Regularized state estimation
  • Trainable priors

Fingerprint

Dive into the research topics of 'Power system state estimation using gauss-newton unrolled neural networks with trainable priors'. Together they form a unique fingerprint.

Cite this