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.


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.

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