Work

Leak identification and quantification in gas network using operational data and deep learning framework

Year: 2024

Type: article

Abstract: In this study, we introduce an innovative deep learning framework designed to achieve precise detection, localization, and rate estimation of gas distribution pipeline system leakages. Our method surpasses conventional statistical approaches, particularly those based on Bayesian inference, by accommodating the system’s intricate behaviors, including variable usage and production from both sources and sinks. Notably, our approach demonstrates remarkable accuracy in localizing leakages even amidst multiple occurrences within the system. Specifically, achieving over 98% accuracy in single-leakage scenarios underscores its effectiveness. Furthermore, through data augmentation involving the introduction of noise into the training dataset, we significantly enhance the model’s performance, particularly when tested against real-world-like noisy data. This study not only showcases the efficacy of our proposed deep learning framework but also underscores its adaptability and robustness in addressing complex challenges in gas pipeline systems.

Source: Sustainable Energy Grids and Networks

DOI: 10.1016/j.segan.2024.101496

Authors: Elham Ebrahimi, Mohammadrahim Kazemzadeh, Antonio Ficarella

Institutions: University of Salento, Italian Institute of Technology, Center for Biomolecular Nanotechnologies

Language: English

Topic: Water Systems and Optimization

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy, Climate action

Open Access status: hybrid

Estimated APC paid: $2,370