利用运行数据和深度学习框架识别和量化天然气管网中的泄漏点

IF 4.8 2区 工程技术 Q2 ENERGY & FUELS Sustainable Energy Grids & Networks Pub Date : 2024-08-13 DOI:10.1016/j.segan.2024.101496
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引用次数: 0

摘要

在本研究中,我们介绍了一种创新的深度学习框架,旨在实现配气管道系统泄漏的精确检测、定位和速率估算。我们的方法超越了传统的统计方法,尤其是那些基于贝叶斯推理的方法,因为它适应了系统错综复杂的行为,包括来自源和汇的可变用量和产量。值得注意的是,我们的方法即使在系统内多次发生泄漏的情况下,也能准确定位泄漏位置。具体来说,在单次泄漏情况下的准确率超过 98%,这充分证明了它的有效性。此外,通过在训练数据集中引入噪声的数据增强方法,我们显著提高了模型的性能,尤其是在针对类似真实世界的噪声数据进行测试时。这项研究不仅展示了我们提出的深度学习框架的功效,还强调了它在应对天然气管道系统复杂挑战时的适应性和鲁棒性。
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Leak identification and quantification in gas network using operational data and deep learning framework

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.

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来源期刊
Sustainable Energy Grids & Networks
Sustainable Energy Grids & Networks Energy-Energy Engineering and Power Technology
CiteScore
7.90
自引率
13.00%
发文量
206
审稿时长
49 days
期刊介绍: Sustainable Energy, Grids and Networks (SEGAN)is an international peer-reviewed publication for theoretical and applied research dealing with energy, information grids and power networks, including smart grids from super to micro grid scales. SEGAN welcomes papers describing fundamental advances in mathematical, statistical or computational methods with application to power and energy systems, as well as papers on applications, computation and modeling in the areas of electrical and energy systems with coupled information and communication technologies.
期刊最新文献
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