Inverse Problem of Oil Pipeline Dynamic Operation Based on Model-Free Adaptive Control Theory

被引:0
|
作者
He, Lei [1 ,2 ]
Zhang, Ming [1 ]
Yi, HuaLei [1 ]
机构
[1] CNOCC Res Inst Co Ltd, Beijing, Peoples R China
[2] China Univ Petr, Beijing, Peoples R China
来源
2022 12TH INTERNATIONAL CONFERENCE ON POWER AND ENERGY SYSTEMS, ICPES | 2022年
关键词
oil pipeline system; dynamic operation; inverse problem; model free adaptive control; frequency domain analysis;
D O I
10.1109/ICPES56491.2022.10072412
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The purpose of this paper is to solve the inverse problem of the dynamic operation of dispatchers in oil pipeline system based on the real-time system process parameters. To realize online solution of system dynamic operation inverse problem, model free adaptive control theory is introduced for the first time. In particular, the frequency domain analysis method is adopted to prove that the oil pipeline system meets assumptions of the control theory. The dynamic state model suitable for solving the control process of the pipeline system is established by using the field data, and the inverse problem of the controlled components dynamic process is solved by using the model free adaptive control method. By analyzing the characteristics of pipeline flow process and comparing different linearized models, the full from dynamic linearization method is selected to model the system state. Single-input-single-output system model and Multi-Input-Single-Output system model are adopted to solve the single station and multiple stations joint operation process respectively. This method is verified with SCADA data of a real pipeline system. The results show that the proposed method can realize the online twining of valve dynamic operation process, and improve the accurate of system state parameters estimation during dynamic operation.
引用
收藏
页码:367 / 371
页数:5
相关论文
共 50 条
  • [1] Model-Free Adaptive Control Based on Local Dynamic Linearization
    Chi, Ronghu
    Zhang, Shuhua
    Hui, Yu
    PROCEEDINGS OF THE 30TH CHINESE CONTROL AND DECISION CONFERENCE (2018 CCDC), 2018, : 1552 - 1556
  • [2] Research on Automatic Train Operation based on model-free adaptive control
    Shi W.
    Tiedao Xuebao/Journal of the China Railway Society, 2016, 38 (03): : 72 - 77
  • [3] A Novel Energy Efficient Operation Strategy for a Train Based on Model-Free Adaptive Predictive Control
    Yang Wen
    Yin Chenkun
    Hou Zhongsheng
    PROCEEDINGS OF THE 31ST CHINESE CONTROL CONFERENCE, 2012, : 7286 - 7291
  • [4] Research of Dynamic Voltage Restorer base on the Model-free adaptive control
    Zhou Xi Chao
    Wang Wei Zhou
    Liu Jun
    Liu Fu Chao
    RENEWABLE ENERGY AND ENVIRONMENTAL TECHNOLOGY, PTS 1-6, 2014, 448-453 : 2180 - 2184
  • [5] Model-free Adaptive Control for Spacecraft Attitude
    Ran Xie
    Ting Song
    Peng Shi
    Yushan Zhao
    Journal of Harbin Institute of Technology(New series), 2016, 23 (06) : 61 - 66
  • [6] Secondary Frequency Control for Islanded MG Based on Model-free Adaptive Control
    Shi, Yong
    Hu, Sunan
    Chen, Lei
    Ma, Kun
    2020 5TH ASIA CONFERENCE ON POWER AND ELECTRICAL ENGINEERING (ACPEE 2020), 2020, : 612 - 616
  • [7] Evaluation-Function-based Model-free Adaptive Fuzzy Control
    Naba, Agus
    JOURNAL OF ENGINEERING AND TECHNOLOGICAL SCIENCES, 2016, 48 (06): : 679 - 699
  • [8] GRU-based model-free adaptive control for industrial processes
    Jinggao Sun
    Ziqing Wei
    Xing Liu
    Neural Computing and Applications, 2023, 35 : 17701 - 17715
  • [9] Dynamical linearization based PLS modeling and model-free adaptive control
    Lin, Mingming
    Chi, Ronghu
    Lin, Na
    Liu, Zhiqing
    2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS, 2023, : 1528 - 1533
  • [10] GRU-based model-free adaptive control for industrial processes
    Sun, Jinggao
    Wei, Ziqing
    Liu, Xing
    NEURAL COMPUTING & APPLICATIONS, 2023, 35 (24) : 17701 - 17715