Static ATC Estimation Using Fully Complex-Valued Radial Basis Function Neural Network

  • M. Karuppasamypandiyan
  • R. Narmatha Banu
  • P. M. Manobalaa
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 325)


In the deregulated power systems, the available transfer capability (ATC) should be known for secure and reliable operation. ATC mainly depends on load for a particular transaction. Due to complex nature of load, it is better if the ATC estimator is able to handle this complex nature. This paper presents fully complex-valued radial basis function (FC-RBF) neural network approach for ATC estimation for bilateral transaction under normal condition. The training patterns for neural network are generated using differential evolution algorithm (DEA). The important feature of the proposed method is the use of input reduction techniques to improve the performance of the developed network. Differential evolution feature selection (DEFS) technique is proposed to reduce the complexity and training time of neural network. The proposed method is tested on IEEE 118 bus system, and results are compared with DEA and repeated power flow (RPF) results. The test results show that the proposed method reduces the training time and it is suitable for online application.


Available transfer capability Differential evolution feature selection Fully complex-valued radial basis function Repeated power flow 


  1. 1.
    R.D. Christie, B.F. Wollenberg, I. Wangstien, Transmission management in the deregulated environment. Proc. IEEE 88(2), 170–195 (2000)CrossRefGoogle Scholar
  2. 2.
    G.C. Ejebe, J. Tong, J.G. Waight, J.G. Frame, X. Wang, W.F. Tinney, Available transfer capability calculation. IEEE Trans. Power Syst. 13(4), 1521–1527 (1998)CrossRefGoogle Scholar
  3. 3.
    A. Kumar, S.C. Srivastava, S.N. Singh, Available transfer capability determination in competitive electricity market using AC distribution factors. Electr. Power Compon. Syst. 32, 927–939 (2004)CrossRefGoogle Scholar
  4. 4.
    B. Mozafari, A.M. Ranjbar, A.R. Shirani, A. Barkeseh, A comprehensive method for available transfer capability calculation in a deregulated power system, in IEEE International Conference on Electric Utility Deregulation, Restructuring and Power Technologies, vol. 2 (2004), pp. 680–685Google Scholar
  5. 5.
    H.H. Chen, G.Q. Li, H.L. Liao, A self-adaptive improved particle swarm optimization algorithm and its application in available transfer capability calculation, in IEEE Computer Society ICNC, vol. 3 (2009), pp. 200–205Google Scholar
  6. 6.
    R. Rajathy, R. Gnanadass, K. Manivannan, H. Kumar, Differential evolution based method for total transfer capability evaluation. Int. J. Eng., Sci. Technol. 2(5), 81–91 (2010)CrossRefGoogle Scholar
  7. 7.
    X. Luo, A.D. Patton, C. Singh, Real power transfer capability calculations using multi-layer feed-forward neural networks. IEEE Trans. Power Syst. 15(2), 903–908 (2000)CrossRefGoogle Scholar
  8. 8.
    D. Devaraj, J. Preetha Roselyn, On-line voltage stability assessment using radial basis function network model with reduced input features. Electr. Power Energy Syst. 33, 1550–1555 (2011)CrossRefGoogle Scholar
  9. 9.
    R.N. Khushaba, A. Al-Ani, A. Al-Jumaily, Feature subset selection using differential evolution and a statistical repair mechanism. Expert Syst. Appl. 38, 11515–11526 (2011)CrossRefGoogle Scholar
  10. 10.
    R. Storn, K. Price, Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997)CrossRefMATHMathSciNetGoogle Scholar
  11. 11.
    S. Suresh, N. Sundararajan, R. Savitha, A fully complex-valued radial basis function network and its learning algorithm. Supervised Learn. Complex-valued Neural Netw. Stud. Comput. Intell. 421, 49–71 (2013)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • M. Karuppasamypandiyan
    • 1
  • R. Narmatha Banu
    • 2
  • P. M. Manobalaa
    • 2
  1. 1.Kalasalingam UniversitySrivilliputhurIndia
  2. 2.Vellammal College of Engineering and TechnologyMaduraiIndia

Personalised recommendations