Research on Reactive Power Optimization Based on Immunity Genetic Algorithm

  • Keyan Liu
  • Wanxing Sheng
  • Yunhua Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)


This paper proposed a new kind of immune genetic algorithm (IGA) according to the current algorithms solving the reactive power optimization. The hybrid algorithm is applied in reactive power optimization of power system. Adaptive crossover and adaptive mutation are used according to the fitness of individual. The substitution of individuals is implemented and the multiform of the population is kept to avoid falling into local optimum. The decimal integer encoding and reserving the elitist are used to improve the accuracy and computation speed. The flow chart of improved algorithm is presented and the parameter of the immune genetic algorithm is provided. The procedures of IGA algorithm are designed. A standard test system of IEEE 30-bus has been used to test. The results show that the improved algorithm in the paper is more feasible and effective than current known algorithms.


Genetic Algorithm Power System Optimal Power Flow Immune Algorithm Simple 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Davis, L.: The Handbook of Genetic Algorithms. Van Nostrand Reingold, New York (1991)Google Scholar
  2. 2.
    Lai, L.L., Ma, J.T.: Application of Evolutionary Programming to Reactive Power Planning-comparison with Nonliear Programming Approach. IEEE Transactions on Power Systems 12(1), 198–204 (1997)CrossRefGoogle Scholar
  3. 3.
    Iba, K.: Reactive Power Optimization by Genetic Algorithm. IEEE Transactions on Power Systems 9(2), 685–692 (1994)CrossRefGoogle Scholar
  4. 4.
    Lee, K.Y., Xiaomin, B.X.M., Park, Y.M.: Optimization Method for Reactive Power Planning by Using a Modified Simple Genetic Algorithm. IEEE Transactions on Power Systems 10(4), 1843–1850 (1995)CrossRefGoogle Scholar
  5. 5.
    Wei, H., Eric, H., Khayam, G.: Using Immune Genetic Algorithm to Optimize the Reactive Power. In: Australasian Universities Power Engineering Conference (AUPEC 2004), Brisbane Australia (2004)Google Scholar
  6. 6.
    Zhong, H.M., Ren, Z., Zhang, Y.G., Liu, B.F.: Immune Algorithm and Its Application in Power System Reactive Power Optimization. Power System Technology, 16–19 (2004) (in Chinese) Google Scholar
  7. 7.
    Dipankar, D.: Special Issue on Artificial Immune System. IEEE Transactions on Evolutionary Computation 6(3), 225–226 (2002)CrossRefGoogle Scholar
  8. 8.
    Liao, G.C.: Short-term Thermal Generation Scheduling Using Improved Immune Algorithm. Electric Power Systems Research 76(5), 360–373 (2006)CrossRefGoogle Scholar
  9. 9.
    Anastasios, G.B., Pandel, N.B., Christoforos, E.Z., Vasilios, P.: Optimal Power Flow by Enhanced Genetic Algorithm. IEEE Transactions on Power Systems 17(2), 229–236 (2002)CrossRefGoogle Scholar
  10. 10.
    Wu, Q.H., Cao, Y.J., Wen, J.Y.: Optimal Reactive Power Dispatch Using an Adaptive Genetic Algorithm. Int. J. Electr. Power & Energy Syst. 20(8), 563–569 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Keyan Liu
    • 1
  • Wanxing Sheng
    • 2
  • Yunhua Li
    • 1
  1. 1.Beijing University of Aeronautics and Astronautics, Beijing, ChinaBeijingChina
  2. 2.China Electric Power Research Institute, Beijing, ChinaBeijingChina

Personalised recommendations