Physics-Informed Recurrent Network for State-Space Modeling of Gas Pipeline Networks

TL;DR

This paper introduces a physics-informed recurrent neural network that accurately models gas pipeline dynamics by combining data-driven learning with physical fluid-dynamic equations, improving parameter efficiency and robustness.

Contribution

It presents a novel PIRN framework that embeds physical models into RNNs for end-to-end pipeline parameter identification from sparse data.

Findings

Accurately estimates pipeline models from limited measurements

Demonstrates robustness and high parameter efficiency

Integrates seamlessly into optimization frameworks

Abstract

As a part of the integrated energy system (IES), gas pipeline networks can provide additional flexibility to power systems through coordinated optimal dispatch. An accurate pipeline network model is critical for the optimal operation and control of IESs. However, inaccuracies or unavailability of accurate pipeline parameters often introduce errors in the state-space models of such networks. This paper proposes a physics-informed recurrent network (PIRN) to identify the state-space model of gas pipelines. It fuses sparse measurement data with fluid-dynamic behavior expressed by partial differential equations. By embedding the physical state-space model within the recurrent network, parameter identification becomes an end-to-end PIRN training task. The model can be realized in PyTorch through modifications to a standard RNN backbone. Case studies demonstrate that our proposed PIRN can accurately estimate gas pipeline models from sparse terminal node measurements, providing robust performance and significantly higher parameter efficiency. Furthermore, the identified state-space model of the pipeline network can be seamlessly integrated into optimization frameworks.