Efficient State Estimation for Gas Pipeline Networks via Low-Rank Approximations

TL;DR

This paper explores the use of low-rank model approximations combined with Kalman filtering to improve the efficiency and accuracy of state estimation in large-scale gas pipeline networks.

Contribution

It introduces a novel approach that integrates low-rank model order reduction with Kalman filtering for gas pipeline networks, enhancing computational efficiency and estimation quality.

Findings

Low-rank approximations reduce computational effort significantly.

The proposed method outperforms existing low-rank Kalman filters in accuracy.

Efficient state estimation is achieved for large-scale gas pipeline systems.

Abstract

In this paper we investigate the performance of projection-based low-rank approximations in Kalman filtering. For large-scale gas pipeline networks structure-preserving model order reduction has turned out to be an advantageous way to compute accurate solutions with much less computational effort. For state estimation we propose to combine these low-rank models with Kalman filtering and show the advantages of this procedure to established low-rank Kalman filters in terms of efficiency and quality of the estimate.