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Cooperative Co-evolutionary Approach Applied in Reactive Power Optimization of Power System

  • Jianxue Wang
  • Weichao Wang
  • Xifan Wang
  • Haoyong Chen
  • Xiuli Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)

Abstract

Cooperative Co-evolutionary Approach (CCA) is a new architecture of evolutionary computation. Based on CCA, the paper proposes a new method for reactive power optimization problem in power system, which is non-convex, non-linear, discrete, and usually with a large number of control variables. According to the decomposition-coordination principle, the reactive power optimization problem is decomposed into a number of sub-problems, which is optimized by a single evolutionary algorithm population. The populations interact with each other through a common system model and co-evolve and result in the continuous evolution of the whole system. The reactive power optimization problem is solved when the co-evolutionary process ends. Simulation results show that compared with conventional Genetic Algorithm (GA), CCA not only can obtain better optimal results, but also has better convergence property. CCA reduce the over-long computational time of GA and is more suitable for solving large-scale optimization problems.

Keywords

Power System Annealing Selection Simple Genetic Algorithm Reactive Power Compensation Conventional Genetic Algorithm 
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

  • Jianxue Wang
    • 1
  • Weichao Wang
    • 2
  • Xifan Wang
    • 1
  • Haoyong Chen
    • 1
  • Xiuli Wang
    • 1
  1. 1.School of Electrical EngineeringXi’an Jiaotong UniversityXi’anP.R. China
  2. 2.Xi’an Electric Power InstituteXi’anP.R. China

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