Wong, K. C. P.; Ryan, H. M. and Tindle, J.
PDF (Not Set)
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
|Google Scholar:||Look up in Google Scholar|
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
Mathematics, Computing and Technology
|Depositing User:||Patrick Wong|
|Date Deposited:||16 Jul 2009 09:43|
|Last Modified:||23 Feb 2016 21:36|
|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.