The Open UniversitySkip to content

Power system fault prediction using artificial neural networks

Wong, K. C. P.; Ryan, H. M. and Tindle, J. (1996). Power system fault prediction using artificial neural networks. In: Progress in Neural Information Processing. SET (Amari, S. -I.; Xu, L.; Chan, L. -W.; King, I. and Leung, K. -S. eds.), Springer, London, UK, pp. 1181–1186.

Full text available as:
PDF (Not Set) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (195Kb)
Google Scholar: Look up in Google Scholar


The medium term goal of the research reported in this paper was the development of a major in-house suite of strategic computer aided network simulation and decision support tools to improve the management of power systems. This paper describes a preliminary 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. To achieve this goal, an AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector 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 . Simulation will normally take place using equivalent circuit representation. Artificial Neural Networks (ANNs) are used to construct a hierarchical feed-forward structure which is the most important component in the fault detector. Simulation of a transmission line (2-port  circuit ) has already been carried out and preliminary results using this system are promising. This approach provided satisfactory results with accuracy of 95% or higher.

Item Type: Conference Item
ISBN: 981-3083-05-0, 978-981-3083-05-9
Keywords: decision support systems; digital simulation; fault location; feedforward neural nets; power system analysis computing; power system reliability; artificial neural networks; strategic computer aided network simulation tools; strategic computer aided network decision support tools; artificial intelligence method; AI based detector; fault monitoring; external measurements; output nodes; input nodes; malfunction prediction; equivalent circuit representation; hierarchical feedforward structure; transmission line simulation
Academic Unit/Department: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 17762
Depositing User: Patrick Wong
Date Deposited: 16 Jul 2009 09:10
Last Modified: 05 Oct 2016 07:50
Share this page:

Download history for this item

These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.

▼ Automated document suggestions from open access sources

Actions (login may be required)

Policies | Disclaimer

© The Open University   + 44 (0)870 333 4340