Skip to main content

Multi-output On-Line ATC Estimation in Deregulated Power System Using ANN

  • Conference paper
Advances in Intelligent Informatics

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 320))

  • 1856 Accesses

Abstract

Fast and accurate evaluation of the Available Transfer Capability (ATC) is essential for the efficient use of networks in a deregulated power system. This paper proposes multi output Feed Forward neural network for on line estimation of ATC. Back Propagation Algorithm is used to train the Feed Forward neural network. The data sets for developing Artificial Neural Network (ANN) models are generated using Repeated Power Flow (RPF) algorithm. The effectiveness of the proposed ANN models are tested on IEEE 24 bus Reliability Test System (RTS). The results of ANN model is compared with RPF results. From the results, it is observed that the ANN model developed is suitable for fast on line estimation of ATC.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bhattacharya, K., Bollen, M., Daalder, J.E.: Operation of Restructured Power Systems. Kluwer Academic Publishers (2001)

    Google Scholar 

  2. North American Electricity Reliability Council (NERC). Available transfer capability- Definitions and determinations. NERC Report (June 1996)

    Google Scholar 

  3. Hamoud, G.: Assessment of available transfer capability of transmission systems. IEEE Transactions on Power Systems 15, 27–32 (2000)

    Article  Google Scholar 

  4. Gravened, M.H., Nwankpa, C., Yoho, T.: ATC Computational issues. In: Proc. 32nd Hawaii Int. Conf. System Science, Maui, HI, pp. 1–6 (1999)

    Google Scholar 

  5. Wood, A.J., Woolenberd, B.F.: Power Generation operation and Control, 2nd edn. John Wiley& Sons

    Google Scholar 

  6. Kumar, A., Srivastava, S.C., Singh, S.N.: ATC transmission capability determination in a competitive electricity market using AC distribution factors. Electric Power Components &Systems 32(9), 927–939 (2004)

    Article  Google Scholar 

  7. Ejebe, G.C., Waight, J.G., Frame, J.G., Wang, X., Tinney, W.F.: Available Transfer Capability Calculations. IEEE Transaction on Power Systems 13, 1521–1527 (1998)

    Article  Google Scholar 

  8. Joo, S.K., Liu, C.C.: Optimization techniques for available transfer capability and market calculations. IMA Journal of Management Mathematics 15, 321–337 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  9. Khaburi, M.A., Haghifam, M.R.: A probabilistic modeling based approach for Total Transfer Capability enhancement using FACTS devices. Electrical Power and Energy Systems 32, 12–16 (2010)

    Article  Google Scholar 

  10. Narasimha Rao, K., Amarnath, J., Kiran Kumar, K., Kamakshiah, S.: Available Transfer Capability Calculations Using Neural Networks in Deregulated Power. In: 2008 International Conference on Condition Monitoring and Diagnosis, Beijing, China, April 21-24 (2008)

    Google Scholar 

  11. Luo, X., Patton, A.D., Singh, C.: Real Power Transfer Capabiltiy Calculations using Multi-Layer Feed –Forward Neural Networks. IEEE Transactions on Power Systems 15(2) (May 2000)

    Google Scholar 

  12. Jain, T., Singh, S.N., Srivastava, S.C.: A Neural Network based method for fast ATC estimation in Electricity Markets. IEEE Transactions on Power Systems 15(2) (May 2007)

    Google Scholar 

  13. Masters, T.: Practical neural network recipes in C++. Academic Press, New York (1993)

    Google Scholar 

  14. Devaraj, D., Preetha Roselyn, J., Uma Rani, R.: Artificial neural network model for voltage security based contingency ranking. Electrical Power Energy System 7, 722–727 (2007)

    Google Scholar 

  15. http://www.ee.washington.edu/research/pstca

  16. Zimmerman, R.D., Murillo-Sanchez, C.E., Thomas, R.J.: MATPOWER: MATLAB power system simulation package

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Prathiba, R., Moses, B.B., Devaraj, D., Karuppasamypandiyan, M. (2015). Multi-output On-Line ATC Estimation in Deregulated Power System Using ANN. In: El-Alfy, ES., Thampi, S., Takagi, H., Piramuthu, S., Hanne, T. (eds) Advances in Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 320. Springer, Cham. https://doi.org/10.1007/978-3-319-11218-3_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-11218-3_21

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11217-6

  • Online ISBN: 978-3-319-11218-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics