Wong, K. C. P.; Ryan, H. M. and Tindle, J.
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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:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Depositing User:||Patrick Wong|
|Date Deposited:||16 Jul 2009 09:43|
|Last Modified:||05 Oct 2016 19:48|
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