基于数据特征融合的管网信息物理异常诊断方法

马大中; 胡旭光; 孙秋野; 郑君; 王睿

doi:10.16383/j.aas.2018.c180383

基于数据特征融合的管网信息物理异常诊断方法

doi: 10.16383/j.aas.2018.c180383

东北大学信息科学与工程学院沈阳 110819

基金项目:

国家自然科学基金重大项目 61627809

国家重点研发计划 2017YFF0108800

国家自然科学基金项目 61773109

中央高校基本科研业务费专项基金 N160404005

国家自然科学基金项目 61573094

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

郑君  东北大学信息科学与工程学院硕士研究生.主要研究方向为基于机器学习的综合能源系统故障检测与诊断.E-mail:ZJ623928036@163.com

王睿  东北大学信息科学与工程学院博士研究生.2016年于东北大学获得电气工程及其自动化专业学士学位.主要研究方向为能源互联网中分布式电源的协同优化及其电磁时间尺度稳定性分析.E-mail:1610232@stu.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, 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

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出版历程
- 收稿日期: 2018-05-31
- 录用日期: 2018-09-12
- 刊出日期: 2019-01-20

Cyber-physical Abnormity Diagnosis Method Using Data Feature Fusion for Pipeline Network

College of Information Science and Engineering, Northeastern University, Shenyang 110819

Funds:

Major Program of National Natural Foundation of China 61627809

National Key Research and Development Program of China 2017YFF0108800

National Natural Science Foundation of China 61773109

Fundamental Research Funds for the Central Universities N160404005

National Natural Science Foundation of China 61573094

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

Master student at the College of Information Science and Engineering, Northeastern University. His research interest covers fault detection and diagnosis of integrated energy system based on machine learning

Ph. D. candidate at the College of Information Science and Engineering, Northeastern University. He received his bachelor degree in electrical engineering and automation from Northeastern University in 2016. His research interest covers collaborative optimization of distributed generation and its stability analysis of electromagnetic timescale in energy internet

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

摘要: 随着管网物理空间和信息网络的深度融合，系统面临着物理和信息空间异常带来的运行风险.本文根据管网系统数据量大、耦合性强的特点，提出一种基于数据特征融合的信息物理异常诊断方法.首先通过站场信息数据构建信息增维矩阵并且通过矩阵预分析实现信息传输中断异常的判断.然后基于不同站场信息构建的信息增维协方差矩阵，通过矩阵特征值分布的变化情况对物理异常以及信息传输错误异常进行区分.在此基础上，为了对管网物理异常分类实现系统运行状态的有效分析，将管网信息增维协方差矩阵最大特征向量映射的二维图像作为输入，采用卷积神经网络进行研究，进而实现对物理异常的准确判断.最后通过某实际管网数据进行仿真分析，验证所提方法的有效性.
- 信息物理系统 /
- 输油管网 /
- 数据驱动 /
- 异常诊断
Abstract: With deep fusion between pipeline physical network and cyber network, the system is facing operational risks caused by cyber-physical anomalies. According to pipe network features of big data and strong coupling, this paper proposes a cyber-physical abnormity diagnosis method using data feature fusion for pipeline network. Firstly, a cyber augmented matrix can be built by station cyber data, and cyber interrupt is diagnosed by matrix pre-analysis. Furthermore, the cyber augmented covariance matrix is established in light of the different station cyber, and physical anomaly and cyber transmission error anomaly are distinguished from each other by the variation condition of the matrix eigenvalue distribution. To effectively analyze the operating state of the physical network anomaly classification, the two-dimensional images which are mapped by the maximum eigenvectors of the cyber augmented covariance matrix of the pipeline network are regarded as the input signals, meanwhile, a convolutional neural network is utilized to carry out the analysis, thus the accurate judgment of the physical anomaly is realized. Eventually, the effectiveness of the proposed method is demonstrated through the time-domain simulation result obtained on a practical pipeline network.
- Cyber-physical systems (CPS) /
- oil pipeline network /
- data driven /
- abnormity diagnosis
注释:

1) 本文责任编委曹向辉

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图 1 管网信息物理系统结构

Fig. 1 CPS structure of pipeline network

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图 2 管网卷积神经网络模型

Fig. 2 structure of pipeline network

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图 3 管网信息物理异常诊断流程图

Fig. 3 Flowchart of pipeline network for cyber-physical abnormity diagnosis

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图 4 管道信息中断压力曲线

Fig. 4 Pipeline cyber interrupt pressure

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图 5 管道信息错误压力曲线

Fig. 5 Pipeline cyber error pressure

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图 6 管道压力最大特征值曲线

Fig. 6 Max eigenvalue curves of pipeline pressure

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图 7 管网工况调整压力曲线

Fig. 7 Pipeline network operation adjustment pressure

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图 8 管网压力最大特征值曲线

Fig. 8 Max eigenvalue curves of pipeline network pressure

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图 9 卷积神经网络输出特征图

Fig. 9 Output feature of CNN

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图 10 管道泄漏压力曲线

Fig. 10 Pipeline leakage pressure

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图 11 管道压力最大特征值曲线

Fig. 11 Max eigenvalue curves of pipeline network pressure

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图 12 卷积神经网络输出特征图

Fig. 12 Output feature of CNN

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图 13 管道泄漏压力曲线

Fig. 13 Pipeline leakage pressure

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图 14 管道压力最大特征值曲线

Fig. 14 Max eigenvalue curve of pipeline network pressure

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图 15 卷积神经网络输出特征图

Fig. 15 Output feature of CNN

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表 1 物理异常统计结果

Table 1 The statistical result of physical abnormity

待识别类型	正确分类数	错误分类数	精度(%)
工况调整	2 696	104	96.3
泄漏	2 355	145	94.2

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表 2 不同方法物理异常精度对比

Table 2 Comparison of accuracy among different methods

诊断方法	灵敏度	特异度	准确率
本文	94.2 %	96.3 %	95.3 %
文献[7]	91.0 %	88.6 %	89.7 %
BPNN	92.4 %	91.8 %	92.1 %
SVM	93.0 %	92.6 %	92.8 %

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表 3 不同方法物理异常计算时间对比

Table 3 Comparison of computing time among different methods

诊断方法	训练时间	测试时间
本文	438.7582 s	$1.6701\times10^{-2}$ s
文献[7]	20.1538 s	$2.1252\times10^{-6}$ s
BPNN	16.9072 s	$8.6301\times10^{-7}$ s
SVM	1.4099 s	$1.9650\times10^{-5}$ s

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基于数据特征融合的管网信息物理异常诊断方法

doi: 10.16383/j.aas.2018.c180383

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, 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

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Cyber-physical Abnormity Diagnosis Method Using Data Feature Fusion for Pipeline Network

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留言板

基于数据特征融合的管网信息物理异常诊断方法

doi: 10.16383/j.aas.2018.c180383

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, 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

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出版历程

Cyber-physical Abnormity Diagnosis Method Using Data Feature Fusion for Pipeline Network

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出版历程

目录

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, 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