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Design of UPFC controller in large-scale power systems based on immune genetic algorithm.

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Title: Design of UPFC controller in large-scale power systems based on immune genetic algorithm.
Authors: Quanyuan Jiang1 jqy@zju.edu.cn, Zhenyu Zou1, Zhiyong Wang1, Yijia Cao1
Source: Transactions of the Institute of Measurement & Control. 2006, Vol. 28 Issue 1, p15-25. 11p. 2 Diagrams, 1 Chart, 3 Graphs.
Subjects: Genetic programming, Computer simulation of immune system, Genetic algorithms, Combinatorial optimization, Mathematical optimization, Electric power system control
Abstract: This paper proposed a new optimization algorithm-immune genetic algorithm (IGA), which is based on immune genetic theory of creatures and can simulate the immune system and its behaviour in organisms. Compared with common genetic algorithms, the IGA adopts the following techniques to improve the global searching ability and convergence speed: immune memory, immune selection, concentration control, niching technique, chaos production and metabolism. Several typical test functions are used to verify the excellent performances of the proposed IGA. Finally, the IGA is applied to optimize the parameters of a unified power flow controller (UPFC) to improve the stability of the New England Test Power System (NETPS). Numerical simulation results demonstrate the validity of the optimized UPFC controller. [ABSTRACT FROM AUTHOR]
Copyright of Transactions of the Institute of Measurement & Control is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
An: 19873320
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  Data: Design of UPFC controller in large-scale power systems based on immune genetic algorithm.
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  Data: <searchLink fieldCode="AR" term="%22Quanyuan+Jiang%22">Quanyuan Jiang</searchLink><relatesTo>1</relatesTo><i> jqy@zju.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhenyu+Zou%22">Zhenyu Zou</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhiyong+Wang%22">Zhiyong Wang</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Yijia+Cao%22">Yijia Cao</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Transactions+of+the+Institute+of+Measurement+%26+Control%22">Transactions of the Institute of Measurement & Control</searchLink>. 2006, Vol. 28 Issue 1, p15-25. 11p. 2 Diagrams, 1 Chart, 3 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Genetic+programming%22">Genetic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation+of+immune+system%22">Computer simulation of immune system</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Combinatorial+optimization%22">Combinatorial optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+power+system+control%22">Electric power system control</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper proposed a new optimization algorithm-immune genetic algorithm (IGA), which is based on immune genetic theory of creatures and can simulate the immune system and its behaviour in organisms. Compared with common genetic algorithms, the IGA adopts the following techniques to improve the global searching ability and convergence speed: immune memory, immune selection, concentration control, niching technique, chaos production and metabolism. Several typical test functions are used to verify the excellent performances of the proposed IGA. Finally, the IGA is applied to optimize the parameters of a unified power flow controller (UPFC) to improve the stability of the New England Test Power System (NETPS). Numerical simulation results demonstrate the validity of the optimized UPFC controller. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Transactions of the Institute of Measurement & Control is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1191/0142331206tm159oa
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 11
        StartPage: 15
    Subjects:
      – SubjectFull: Genetic programming
        Type: general
      – SubjectFull: Computer simulation of immune system
        Type: general
      – SubjectFull: Genetic algorithms
        Type: general
      – SubjectFull: Combinatorial optimization
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Electric power system control
        Type: general
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      – TitleFull: Design of UPFC controller in large-scale power systems based on immune genetic algorithm.
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            NameFull: Quanyuan Jiang
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            NameFull: Zhenyu Zou
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            NameFull: Zhiyong Wang
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            NameFull: Yijia Cao
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            – D: 01
              M: 01
              Text: 2006
              Type: published
              Y: 2006
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              Value: 28
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            – TitleFull: Transactions of the Institute of Measurement & Control
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