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Design of Self-adaptive Single Neuron Facts Controllers Based on Genetic Algorithm

  • Quan-Yuan Jiang
  • Chuang-Xin Guo
  • Yi-Jia Cao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

With the growing application of Static Var Compensator (SVC) and Static Synchronous Compensator (STATCOM), the coordinating problem of SVC and STATCOM controllers in joint operation must be considered in modern power systems. This paper firstly establishes the nonlinear differential-algebra equations model of a single-machine infinite-bus (SMIB) power system installed with a SVC and a STATCOM and points out the possibility of the negative interactions between SVC and STATCOM controllers in this SMIB power system. Hence, a self-adaptive single neuron (SSN) control approach based on genetic algorithm is designed to eliminate the negative interactions and improve the stability of the closed-loop SMIB power system. The detailed simulation results demonstrate the effectiveness of the proposed SSN controllers.

Keywords

Genetic Algorithm Power System Negative Interaction Power System Stability Voltage Controller 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Quan-Yuan Jiang
    • 1
  • Chuang-Xin Guo
    • 1
  • Yi-Jia Cao
    • 1
  1. 1.College of Electrical EngineeringZhejiang UniversityHangzhouChina

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