Skip to main content
Log in

Power system aggregate load area modelling by particle swarm optimization

  • Published:
International Journal of Automation and Computing Aims and scope Submit manuscript

Abstract

This paper presents a new approach for deriving a power system aggregate load area model (ALAM). In this approach, an equivalent area load model is derived to represent the load characters for a particular area load of a power system network. The Particle Swarm Optimization (PSO) method is employed to identify the unknown parameters of the generalised system, ALAM, based on the system measurement directly using a one-step scheme. Simulation studies are carried out for an IEEE 14-Bus power system and an IEEE 57-Bus power system. Simulation results show that the ALAM can represent the area load characters accurately under different operational conditions and at different power system states.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. J. Kennedy, R. C. Eberhart. Particle swarm optimization. In Proceedings of IEEE International Conference on Neural Networks, Perth, Austrilia, IEEE Press, vol. 4, pp. 1942–1948.

  2. Y. Shi, R. C. Eberhart. Empirical study of particle swarm optimization. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 1999), IEEE Press, Piscataway, NJ. pp. 1945–1950, 1999.

    Chapter  Google Scholar 

  3. Andries P. Engelbrecht. Computational Intelligence. John Wiley & Sons Ltd, UK, pp. 185–209, 2002.

    Google Scholar 

  4. J. Kennedy, R. C. Eberhart, Y. Shi. Swarm Intelligence, Morgan Kaufmann Publishers, USA, pp. 369–392, 2001.

    Google Scholar 

  5. K. E. Parsopoulos, V. P. Plagianakos, G. D. Magoulas, M. N. Vrahatis. Stretching technique for obtaining global minimizers through particle swarm optimization. In Proceedings of the Workshop on Particle Swarm Optimization 2001, Indianapolis USA, pp. 22–29, 2001.

  6. K. E. Parsopoulos, M. N. Vrahatis. Particle swarm optimizer in noisy and continuously changing environments. In Proceeding of the IASTED International Conference on Artificial Intelligence and Soft Computing (ASC 2001), Cancun, Mexico. pp. 289–294, 2001.

  7. Y. Shi, R.C. Eberhart, Y. Chen. Implementation of evolutionary fuzzy systems. IEEE Transactions on Fuzzy Systems, vol. 7, no. 2, pp. 109–119, 1999.

    Article  Google Scholar 

  8. P. Kundur, N. J. Balu, M. G. Lauby. Power System Stability and Control, McGraw-Hill, Inc, USA, pp. 271–313, 1993.

    Google Scholar 

  9. W. W. Price et al. Load representation for dynamic performance analysis. IEEE Transaction on Power Systems, vol. 8, no. 2, pp. 472–482, 1993.

    Article  MathSciNet  Google Scholar 

  10. IEEE Task Force on Load Representation for Dynamic Performance, Bibliography on load models for power flow and dynamic simulation. IEEE Transactions on Power Systems, vol. 10, no. 1, pp. 523–538, 1995.

  11. J. Y. Wen, L. Jiang, Q. H. Wu, S. J. Cheng. Power system load modelling by learning based on system measurements. IEEE Transactions on Power Delivery, vol. 18, no. 2, pp. 364–371, 2003.

    Article  Google Scholar 

  12. J. Y. Wen, Q. H. Wu, K. I. Nuttall, D. W. Shimmin, S. J. Cheng. Construction of power system load models and network equivalence using an evolutionary computation technique. Electrical Power and Energy Systems, vol. 25, no. 4, pp. 293–299, 2003.

    Article  Google Scholar 

  13. J. L. Wei, J. Wang, L. Yang, Q. H. Wu. Power system aggregate load area modeling. In Proceedings of 2005 IEEE/PES Transmission and Distribution Conference and Exhibition: Asia and Pacific, Dalian World Expo Plaza, Dalian, China, F0511, no. 1–6, August 14–18, 2005.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jian-Lin Wei.

Additional information

Jian-Lin Wei received the B.Eng degree in electromechanical engineering from Guangdong University of Technology (GDUT), China in 2001. He received the M.Sc.(Eng) degree in intelligence engineering from the University of Liverpool with distinction in 2002. With the sponsor from the RWEnpower Plc. and the University of Liverpool, currently he is a research PhD student in the Department of Electrical Engineering and Electronics, University of Liverpool. His research interests include Power System Load Modelling, Coal Mill Modelling and Control, Evolutionary Computation Techniques (Genetic algorithm, Particle Swarm Optimization), and Homogenous Charge Compression Ignition Engine’s Modelling and Control.

Ji-Hong Wang received the B.Eng. degree in automatic control from Wuhan University of Technology, China in 1982. She received the M.Sc. degree in automatic control from Shandong University of Science and Technology, China in 1985 and received the PhD degree in nonlinear uncertain system control from Coventry University, UK in 1995. Currently, she is a senior lecturer in the Department of Electrical. Engineering and Electronics, University of Liverpool, Liverpool, UK. Her main research interests include nonlinear system control, system modeling and identification, power systems, energy efficient systems and applications of intelligent algorithms.

Qing-Hua Wu obtained an M.Sc. (Eng) degree in electrical engineering from Huazhong University of Science and Technology (HUST), China in 1981. From 1981 to 1984, he was appointed lecturer in electrical engineering in the University. He obtained a Ph.D degree from The Queen’s University of Belfast (QUB), U.K. in 1987. He worked as a research fellow and senior research fellow in QUB from 1987 to 1991 and lecturer and senior lecturer in the Department of Mathematical Sciences, Loughborough University, UK from 1991 to 1995. Since 1995 he has held the Chair of Electrical Engineering in the Department of Electrical Engineering and Electronics, The University of Liverpool, UK, acting as the Head of Intelligence Engineering and Automation group. Professor Wu is a Chartered Engineer, Fellow of IEE and Senior Member of IEEE. His research interests include nonlinear adaptive control, mathematical morphology, neural networks, learning systems, pattern recognition, evolutionary computation and power system control and operation.

Nan Lu graduated from the University of Science and Technology Beijing (USTB), China in 2000. He received the M. S. degree from USTB in 2004. He is currently a research student at the University of Liverpool. His research interests include biomedical signal processing and image processing.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wei, JL., Wang, JH., Wu, Q.H. et al. Power system aggregate load area modelling by particle swarm optimization. Int J Automat Comput 2, 171–178 (2005). https://doi.org/10.1007/s11633-005-0171-5

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11633-005-0171-5

Keywords

Navigation