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IJEETC 2023 Vol.12(3): 209-215
doi: 10.18178/ijeetc.12.3.209-215

Application of Recurrent Graph Convolutional Networks to the Neural State Estimation Problem

Alexander Berezin1,*, Stephan Balduin1, Thomas Oberließen2, Eric Veith1, Sebastian Peter2, and Sebastian Lehnhoff1
1. R&D Division Energy - OFFIS e.V., Escherweg 2, 26121, Germany
2. ie3 - TU Dortmund, Emil-Figge-Straße 70, 44227, Germany

Manuscript received September 1, 2022; revised October 15, 2022; accepted November 5, 2022.

Abstract—Neural State Estimation (NSE) is a novel application of deep learning which is concerned with interpolating the state of a distribution power grid from a limited amount of sensor data and can be represented as a non-linear graph time-series nowcasting problem. Although several authors have proposed their solutions for NSE, there is neither a comparison of approaches nor an industry-standard state of the art model yet. The main purpose of this paper is to compare these solutions to a promising new approach: recurrent graph convolutional neural networks. There are theoretical reasons to assume that this class of models is suited for solving NSE. Our experiments verify that they achieve similar performance while also presenting many unique advantages compared to the previously proposed models.

Index Terms—Graph convolutional networks, neural state estimation, power system state estimation

Cite: Alexander Berezin, Stephan Balduin, Thomas Oberließen, Eric Veith, Sebastian Peter, and Sebastian Lehnhoff, "Application of Recurrent Graph Convolutional Networks to the Neural State Estimation Problem," International Journal of Electrical and Electronic Engineering & Telecommunications, Vol. 12, No. 3, pp. 209-215, May 2023. Doi: 10.18178/ijeetc.12.3.209-215

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.