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A systematic hybrid method for real-time prediction of system conditions in natural gas pipeline networks

机译：一种系统混合方法，用于天然气管道网络中系统条件的实时预测

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摘要

The current framework of management of natural gas pipeline systems, based on off-line simulation, is facing challenges because of the increasing complexity, uncertainty and a number of time-dependent factors. To be effective, it requires comprehensive knowledge of system characteristics, accurate initial and boundary conditions. In an attempt to circumvent these problems, in this work we propose to use the deep learning method in the natural gas transmission system operation and management context. A data-driven prediction method is developed from real-time data of operation pressure and gas consumption. Specifically, the deep learning method is combined with the data window method and structural controllability theory to predict the conditions of gas pipeline network components. The data window method is applied to reconstruct the data structure and build a “memory” for the deep learning method. Structural controllability theory is applied to extract critical parameters, for reducing the problem size. The developed method allows accurate and efficient predictions, especially in abnormal conditions. For demonstration, the method is applied to a complex gas pipeline network. The results show that the developed method can provide accurate real-time predictions useful for reducing potential losses in operation, and perform efficient and effective management of the gas pipeline system. In the case study, the average prediction accuracy is higher than 0.99.
机译：基于离线模拟的天然气管道系统管理框架面临挑战，因为增加了复杂性，不确定性和多个时间依赖性因素。为了有效，它需要全面了解系统特征，准确的初始和边界条件。为了试图规避这些问题，在这项工作中，我们建议在天然气传输系统运行和管理环境中使用深度学习方法。从操作压力和气体消耗的实时数据开发了一种数据驱动的预测方法。具体地，深度学习方法与数据窗口方法和结构可控性理论相结合，以预测气体管道网络组件的条件。应用数据窗口方法来重建数据结构并为深度学习方法构建“存储器”。结构可控性理论用于提取关键参数，用于减少问题大小。开发方法允许准确和高效的预测，尤其是在异常条件下。为了演示，该方法应用于复杂的气体管道网络。结果表明，开发方法可以提供可用于降低操作潜在损失的准确实时预测，并对气体管道系统进行有效和有效的管理。在案例研究中，平均预测精度高于0.99。

著录项

  • 来源
  • 作者

    Huai Su; Enrico Zio; Jinjun Zhang; Zhe Yang; Xueyi Li; Zongjie Zhang;

  • 作者单位

    National Engineering Laboratory for Pipeline Safety MOE Key Laboratory of Petroleum Engineering Beijing Key Laboratory of Urban Oil and Gas Distribution Technology China University of Petroleum-Beijing;

    Dipartimento di Energia Politecnico di Milano;

    National Engineering Laboratory for Pipeline Safety MOE Key Laboratory of Petroleum Engineering Beijing Key Laboratory of Urban Oil and Gas Distribution Technology China University of Petroleum-Beijing;

    Dipartimento di Energia Politecnico di Milano;

    National Engineering Laboratory for Pipeline Safety MOE Key Laboratory of Petroleum Engineering Beijing Key Laboratory of Urban Oil and Gas Distribution Technology China University of Petroleum-Beijing;

    National Engineering Laboratory for Pipeline Safety MOE Key Laboratory of Petroleum Engineering Beijing Key Laboratory of Urban Oil and Gas Distribution Technology China University of Petroleum-Beijing;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 石油、天然气工业;
  • 关键词

    Natural gas pipeline system; Deep learning; Data driven; Structural controllability theory; Real-time prediction;

    机译：天然气管道系统;深入学习;数据驱动;结构可控性理论;实时预测;

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