M. Kezunovic and I. Rikalo, “Detect and Classify Faults Using Neural Nets,” IEEE Computer Applications in Power, Vol. 9, No. 4, 1996, pp. 42-47. doi:10.1109/67.539846

has been cited by the following article:

• TITLE: Fault Classification and Localization in Power Systems Using Fault Signatures and Principal Components Analysis

  AUTHORS: Qais H. Alsafasfeh, Ikhlas Abdel-Qader, Ahmad M. Harb

  KEYWORDS: Fault Detection and Classification; Protective Relaying; PCA; PSCAD

  JOURNAL NAME: Energy and Power Engineering, Vol.4 No.6, November 29, 2012

  ABSTRACT: A vital attribute of electrical power network is the continuity of service with a high level of reliability. This motivated many researchers to investigate power systems in an effort to improve reliability by focusing on fault detection, classification and localization. In this paper, a new protective relaying framework to detect, classify and localize faults in an electrical power transmission system is presented. This work will extract phase current values during ( )th of a cycle to generate unique signatures. By utilizing principal component analysis (PCA) methods, this system will identify and classify any fault instantaneously. Also, by using the curve fitting polynomial technique with our index pattern obtained from the unique fault signature, the location of the fault can be determined with a significant accuracy.