The Open UniversitySkip to content
 

Early warning fault detection using artificial intelligent methods

Wong, K. C. P.; Ryan, H. M. and Tindle, J. (1996). Early warning fault detection using artificial intelligent methods. In: 31st Universities Power Engineering Conference '96, 18-20 September 1996, Iraklio, Crete, Greece, Technological Educational Institute of Iraklion, pp. 949–952.

Full text available as:
[img]
Preview
PDF (Not Set) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (196Kb)
Google Scholar: Look up in Google Scholar

Abstract

This paper describes a research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. An AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector for this early warning fault detection device only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system. Artificial Neural Networks (ANNs) are being used as the core of the fault detector. A simulated medium length transmission line has been tested by the detector and the results demonstrate the capability of the detector. Furthermore, comments on an evolutionary technique as the optimisation strategy for ANNs are included in this paper.

Item Type: Conference Item
Keywords: computerised monitoring; fault location; neural nets; power system analysis computing; power system measurement; power system fault detection; artificial intelligence; fault monitoring; fault prediction; external measurements; neural networks; power transmission line; optimisation strategy; computer simulation; evolutionary technique
Academic Unit/Department: Mathematics, Computing and Technology > Computing & Communications
Item ID: 17761
Depositing User: Patrick Wong
Date Deposited: 16 Jul 2009 09:43
Last Modified: 21 Jan 2011 16:38
URI: http://oro.open.ac.uk/id/eprint/17761
Share this page:

Actions (login may be required)

View Item
Report issue / request change

Policies | Disclaimer

© The Open University   + 44 (0)870 333 4340   general-enquiries@open.ac.uk