Artificial Neural Network and Hidden Space SVM for Fault Detection in Power System

  • Qian Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5552)


This paper presents an artificial neural network(ANN) and hidden space support vector machines (HSSVMs) approach for locating faults in radial distribution systems. Different from the traditional Fault Section Estimation methods, the proposed approach uses measurements available at the substation, circuit breaker and relay statuses. The data is analyzed using the principal component analysis (PCA) technique and the faults are classified according to the reactances of their path using a combination of HSSVMs classifiers and feedforward neural networks (FFNNs). The extensive numerical experiments performed for location of different kinds of faults of the transmission line have proved very good accuracy of fault location algorithm. The average error of fault location is about 4%. The result proved its effectiveness.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Zhu, J., Lubkeman, D.L., Girgis, A.A.: Automated Fault Location and Diagnosis on Electric Power Distribution Feeders. IEEE Trans. Power Delivery 12, 801–809 (1997)CrossRefGoogle Scholar
  2. 2.
    Purushothama, G.K., Narendranath, A.U., Thukaram, D., Parthasarathy, K.: ANN Applications in Fault Locators. Elect. Power Syst. Res. 23, 491–506 (2001)CrossRefGoogle Scholar
  3. 3.
    Fan, Y.P., Chen, Y.P.: Contingency Screening of Power System Based on Rough Sets and Fuzzy ARTMA. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3498, pp. 654–661. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)Google Scholar
  5. 5.
    Kezunovic, M., Perunicic, B., Mrkic, J.: An Accurate Fault Location Algorithm Using Synchronized Sampling. Electric Power Systems Research Journal 29, 161–169 (1994)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Qian Wang
    • 1
  1. 1.College of Electrical & Electronics EngineeringNaval Univ. of EngineeringWuhanChina

Personalised recommendations