Two separate continually online-trained neurocontrollers for a unified power flow controller

被引:18
|
作者
Venayagamoorthy, GK [1 ]
Kalyani, RP [1 ]
机构
[1] Univ Missouri, Real Time Power & Intelligent Syst Lab, Rolla, MO 65409 USA
基金
美国国家科学基金会;
关键词
indirect adaptive control; neurocontrollers; neuroidentifiers; power system; Unified Power Flow Controller (UPFC);
D O I
10.1109/TIA.2005.851571
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The crucial factor affecting the modern power systems today is load flow control. The Unified Power Flow Controller (UPFC) provides an effective means for controlling the power flow and improving the transient stability in a power network. The UPFC has fast complex dynamics and its conventional control is based on a linearized model of the power system. This paper presents the design of neurocontrollers to provide better damping during transient and dynamic control. Two separate neurocontrollers are used for controlling the UPFC, one neurocontroller for the shunt inverter and the other for the series inverter. Simulation studies carried out in the PSCAD/EMTDC environment is described and results show the successful control of the UPFC and the power system with two neurocontrollers. Performances of the neurocontrollers are compared with the conventional proportional plus integral controllers for system oscillation damping under different operating conditions for large disturbances.
引用
收藏
页码:906 / 916
页数:11
相关论文
共 50 条
  • [1] Two separate continually online trained neurocontrollers for a Unified Power Flow Controller
    Kalyani, RP
    Venayagamoorthy, GK
    2003 IEEE INDUSTRY APPLICATIONS CONFERENCE, VOLS 1-3: CROSSROADS TO INNOVATIONS, 2003, : 308 - 315
  • [2] Two separate continually online-trained neurocontrollers for excitation and turbine control of a turbogenerator
    Venayagamoorthy, GK
    Harley, RG
    IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 2002, 38 (03) : 887 - 893
  • [3] Two separate continually online trained neurocontrollers for excitation and turbine control of a turbogenerator
    Venayagamoorthy, GK
    Harley, RG
    IAS 2000 - CONFERENCE RECORD OF THE 2000 IEEE INDUSTRY APPLICATIONS CONFERENCE, VOLS 1-5, 2000, : 1263 - 1267
  • [4] A continually online-trained neural network controller for brushless DC motor drives
    Rubaai, A
    Kotaru, R
    Kankam, MD
    IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 2000, 36 (02) : 475 - 483
  • [5] Non-linear high impedance fault distance estimation in power distribution systems: A continually online-trained neural network approach
    Farias, Patrick E.
    de Morais, Adriano Peres
    Rossini, Jean Pereira
    Cardoso, Ghendy, Jr.
    ELECTRIC POWER SYSTEMS RESEARCH, 2018, 157 : 20 - 28
  • [6] A practical Continually Online Trained Artificial Neural Network controller for a turbogenerator
    Venayagamoorthy, GK
    Harley, RG
    IEEE INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS (ISIE 98) - PROCEEDINGS, VOLS 1 AND 2, 1998, : 385 - 389
  • [7] A online-trained neural network controller for electro-hydraulic servo system
    Zhao, H
    Dang, KF
    Lin, TQ
    PROCEEDINGS OF THE 4TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-4, 2002, : 2983 - 2986
  • [8] Reducing the computational demands of continually online-trained artificial neural networks for system identification and control of fast processes
    Burton, B
    Harley, RG
    IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 1998, 34 (03) : 589 - 596
  • [9] Power Flow Control by Unified Power Flow Controller
    Khan, Muhammad Yousaf Ali
    Khalil, Umair
    Khan, Hamayun
    Uddin, Amad
    Ahmed, Sheeraz
    ENGINEERING TECHNOLOGY & APPLIED SCIENCE RESEARCH, 2019, 9 (02) : 3900 - 3904
  • [10] Power Flow Control by Unified Power Flow Controller
    Khalil, Umair
    Khan, Muhammad Yousaf Ali
    Khan, Umer Amir
    Atiq, Shahid
    MEHRAN UNIVERSITY RESEARCH JOURNAL OF ENGINEERING AND TECHNOLOGY, 2020, 39 (02) : 257 - 266