Cooperative co-evolutionary approach applied in reactive power optimization of power system

  • Authors:

  • Jianxue Wang;Weichao Wang;Xifan Wang;Haoyong Chen;Xiuli Wang

  • Affiliations:

  • School of Electrical Engineering, Xi'an Jiaotong University, Xi'an, P.R. China;Xi'an Electric Power Institute, Xi'an, P.R. China;School of Electrical Engineering, Xi'an Jiaotong University, Xi'an, P.R. China;School of Electrical Engineering, Xi'an Jiaotong University, Xi'an, P.R. China;School of Electrical Engineering, Xi'an Jiaotong University, Xi'an, P.R. China

  • Venue:
  • ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
  • Year:
  • 2006

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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.