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基于大维数据驱动的油气管网泄漏监控模糊决策方法

马大中 胡旭光 孙秋野

马大中, 胡旭光, 孙秋野. 基于大维数据驱动的油气管网泄漏监控模糊决策方法. 自动化学报, 2017, 43(8): 1370-1382. doi: 10.16383/j.aas.2017.c160435
引用本文: 马大中, 胡旭光, 孙秋野. 基于大维数据驱动的油气管网泄漏监控模糊决策方法. 自动化学报, 2017, 43(8): 1370-1382. doi: 10.16383/j.aas.2017.c160435
MA Da-Zhong, HU Xu-Guang, SUN Qiu-Ye. A Large Dimensional Data-driven Fuzzy Detection Method for Oil-gas Pipeline Network Leakage. ACTA AUTOMATICA SINICA, 2017, 43(8): 1370-1382. doi: 10.16383/j.aas.2017.c160435
Citation: MA Da-Zhong, HU Xu-Guang, SUN Qiu-Ye. A Large Dimensional Data-driven Fuzzy Detection Method for Oil-gas Pipeline Network Leakage. ACTA AUTOMATICA SINICA, 2017, 43(8): 1370-1382. doi: 10.16383/j.aas.2017.c160435

基于大维数据驱动的油气管网泄漏监控模糊决策方法

doi: 10.16383/j.aas.2017.c160435
基金项目: 

中央高校基本科研业务费专项基金 N160404005

国家自然科学基金 61473069

国家自然科学基金 61573094

国家自然科学基金重大项目 61627809

国家自然科学基金重点项目 61433004

详细信息
    作者简介:

    胡旭光  东北大学信息科学与工程学院博士研究生.主要研究方向为基于数据驱动的故障诊断, 信息物理系统的建模及优化控制.E-mail:1501004@stu.neu.edu.cn

    孙秋野  东北大学信息科学与工程学院教授.主要研究方向为网络控制技术, 分布式控制技术, 分布式优化分析及其在能源互联网, 微网, 配电网等领域相关应用.E-mail:sunqiuye@mail.neu.edu.cn

    通讯作者:

    马大中  东北大学信息科学与工程学院副教授.主要研究方向为故障诊断, 容错控制, 能源管理系统以及分布式发电系统、微网和能源互联网的优化与控制.本文通信作者.E-mail:madazhong@ise.neu.edu.cn

A Large Dimensional Data-driven Fuzzy Detection Method for Oil-gas Pipeline Network Leakage

Funds: 

Fundamental Research Funds for the Central Universities N160404005

National Natural Science Foundation of China 61473069

National Natural Science Foundation of China 61573094

the Major Program of National Natural Science Foundation of China 61627809

the Key Program of National Natural Science Foundation of China 61433004

More Information
    Author Bio:

    Ph.D. candidate at the College of Information Science and Engineering, Northeastern University. His research interest covers fault diagnosis based on data-driven, modeling and optimal control of cyber-physical system

    Professor at the College of Information Science and Engineering, Northeastern University. His research interest covers network control technology, distributed control technology, distributed optimization analysis and various applications in energy internet, microgrid, power distribution network

    Corresponding author: MA Da-Zhong Associate professor at the College of Information Science and Engineering, Northeastern University. His research interest covers fault diagnosis, fault-tolerant control, energy management systems, and control and optimization of distributed generation systems, microgrids and energy internet. Corresponding author of this paper
  • 摘要: 输油管网状态量多及工艺复杂,难以建立精确的管网数学模型,为了能够实时监控管网的安全运行情况,本文提出一种基于大维数据驱动的管网泄漏监控模糊决策方法.首先利用管网现有的数据信息,在不对数据进行降维处理的情况下,从信息物理系统的角度出发,将油气管网的拓扑结构、阀门开度等管道物理数据以及压力、流量等运行信息数据结合起来对复杂管网系统建立数据驱动模型.然后基于大维随机矩阵谱理论,将得到的信息物理数据协方差矩阵谱分布及圆环率作为模糊决策的条件对管网运行情况进行判断.当管网拓扑发生动态变化时,提出的方法可以有效地解决误报率高的问题.最后通过仿真及实例的分析,可以证明所提出方法的有效性.
    1)  本文责任编委  文成林
  • 图  1  管网整体结构

    Fig.  1  The whole structure of pipeline network

    图  2  基于数据驱动的管道管网信息物理系统模型

    Fig.  2  The CPS model of pipeline network based on data-driven

    图  3  管网系统检测示意图

    Fig.  3  Flowchart for leak detection of pipeline network

    图  4  上下游压力曲线图

    Fig.  4  The curve of pipeline pressure

    图  5  管网MSR时间序列

    Fig.  5  MSR series of pipeline network

    图  6  管网矩阵谱分布及圆环

    Fig.  6  Spectral distribution and ring law of pipeline network matrix

    图  7  上下游压力曲线图

    Fig.  7  The curve of pipeline pressure

    图  8  管网MSR时间序列

    Fig.  8  MSR series of pipeline network

    图  9  管网矩阵谱分布及圆环

    Fig.  9  Spectral distribution and ring law of pipeline network matrix

    图  10  上下游压力曲线图

    Fig.  10  The curves of pipeline pressure

    图  11  管网MSR时间序列

    Fig.  11  MSR series of pipeline network

    图  12  管网矩阵谱分布及圆环

    Fig.  12  Spectral distribution and ring law of pipeline network matrix

    图  13  两种方法响应时间与误报警次数的对比

    Fig.  13  The comparison of response time and misalarm number of the methods

    图  14  管网压力示意图

    Fig.  14  The pressure of pipeline network

    表  1  不同维度对比结果

    Table  1  The results of different dimension ratio

    维度比 正常情况特征值均值MSR_N 异常情况特征值均值MSR_A 均值差比值
    0.1 0.97 0.83 0.144
    0.3 0.92 0.78 0.152
    0.5 0.88 0.71 0.193
    0.7 0.84 0.69 1.179
    0.9 0.77 0.64 1.169
    下载: 导出CSV
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  • 收稿日期:  2016-05-28
  • 录用日期:  2016-09-20
  • 刊出日期:  2017-08-20

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