An Improved Particle Swarm Algorithm and Its Application to Power System Transfer Capability Optimization

  • Si-jun Peng
  • Chang-hua Zhang
  • Liang Tang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4115)


In this paper, an algorithm based on PSO (Particle Swarm Optimization) for power system transfer capability calculation is presented. A dual fitness scheme that takes both objective and constraint into account is adopted to evaluate the survival chance of any particle, thus avoid the drawbacks of traditional penalty method. In the evolution process, if the population best particle has no update during a prescribed number of consecutive generations, it is regarded as a local optimum solution and the searching space around this particle is locked to prevent other particles flying into it. And this particle is saved as one of the candidate solution. In the end, by comparing the fitness of all saved particles and the current population best particle the optimum value can be obtained. This improved particle swarm algorithm is then successfully applied to IEEE118 bus system optimization problem. Compared with a traditional well-known method, sequential quadratic programming, our proposal obtains better solutions for this problem.


Particle Swarm Optimization Reactive Power Local Optimization Solution Transfer Capability Global Good Position 
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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  1. 1.
    Transmission Transfer Capability Task Force.: Available Transmission Capability Definitions and Determination. North American Electric Reliability Council. Princeton, New Jersey (1996)Google Scholar
  2. 2.
    North American Electric Reliability Council Available Transfer Capability Working Group: Transmission Capability Margins and Their Use in ATC Determination White Paper. North American Electric Reliability Council, New York (1999) Google Scholar
  3. 3.
    Li, G.: Study On Transmission Transfer Capability Of Large Scale Interconnected Power System Based On Continuation Method. Doctorial thesis. Tianjing University, Tianjing, China (1998)Google Scholar
  4. 4.
    Diao, Q., Mohamed, S., Ni, Y.: Inter-area Total Transfer Capability Calculation Using Sequential Quadratic Programming Method in Power Market. Automation of Electric Power Systems 24, 5–8 (2000)Google Scholar
  5. 5.
    Wang, L., Wu, Z.: Calculation of Available Transfer Capability Taking Into Account Transient Stability Constraints Based on Interior-Point Solution In Electricity Market. In: Proceeding of Electricity Power System Automation, vol. 14 (2004)Google Scholar
  6. 6.
    Mohamed, S., Ni, Y., Wu, F.: Available Transfer Capability Evaluation by Decomposition. IEEE Power Engineering Society Summer Meeting, Vancouver, 1122–1126 (2001)Google Scholar
  7. 7.
    Wang, C., Li, H.: Calculation of Transfer Capability Taking into Account Voltage Stability Constraint. Electric Power Automation Equipment 24 (2004)Google Scholar
  8. 8.
    Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: IEEE International Conference on Neural Networks, Piscataway, pp. 1942–1948 (1995)Google Scholar
  9. 9.
    Pan, Z., Kang, L.: Evolutionary Computation. Tsinghua University Press, Beijing, China (1998)Google Scholar
  10. 10.
    Boeringer, D.W., Werner, D.H.: A Comparison of Particle Swarm Optimization and Genetic Algorithms for A Phased Array Synthesis Problem. In: Antennas and Propagation Society International Symposium, pp. 181–184 (2003)Google Scholar
  11. 11.
    Jin, Y., Cheng, H.: Local Best Embranchment Based Convergence Guarantee Particle Swarm Optimization And Its Use In Transmission Network Planning. In: Proceedings of the CSEE, vol. 25 (2005)Google Scholar
  12. 12.
    Liu, H., Lin, Y.: A Modified Particle Swarm Optimization For Solving Constrained Optimization Problems. Proceedings of Jilin University, vol. 43 (2005)Google Scholar
  13. 13.
    Dong, Y., Tang, J.: Application of Particle Swarm Optimization to Nonlinear Constrained Programming. Proceedings of Northeastern University, vol. 24 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Si-jun Peng
    • 1
  • Chang-hua Zhang
    • 2
  • Liang Tang
    • 3
  1. 1.School of ScienceWuhan University of TechnologyWuhan, Hubei ProvinceChina
  2. 2.Key Laboratory of Complex Systems and Intelligence ScienceInstitute of Automation, Chinese Academy of SciencesBeijingChina
  3. 3.Department of Automotive EngineeringTsinghua UniversityBeijingChina

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