By Topic

Application of artificial neural network in fault location technique

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Li, K.K. ; Dept. of Electr. Eng., Hong Kong Polytech. Univ., Kowloon, China ; Lai, L.L. ; David, A.K.

Recent restructuring of the power industries such as open access and deregulation have an impact on the reliability and security of power systems. New technologies for protection and control schemes are therefore necessary to be introduced in order to maintain the system reliability and security to an acceptable level. Artificial intelligence (AI) techniques naturally become the best choice to improve the performance of the present system used. Most faults which have infeed sources from both ends of the line, especially earth faults with fault resistance, are very difficult to identify. This paper presents a novel approach that can overcome the above difficulties. The artificial neural network (ANN) is used to identify the fault location, as well as the fault resistance in a wide range of system conditions. The training of the ANN is relatively simple and fast. The predicated results from the ANN are proved to be accurate for a wide range of system conditions

Published in:

Electric Utility Deregulation and Restructuring and Power Technologies, 2000. Proceedings. DRPT 2000. International Conference on

Date of Conference: