Abstract
Fault detection and diagnosis in real-time are areas of research interest in knowledge-based expert systems. Rule-based and model-based approaches have been successfully applied to some domains, but are too slow to be effectively applied in a real-time environment. Neural computing is one of the fastest growing areas of artificial intelligence research. This paper explores the suitability of using artificial neural networks for fault detection and diagnosis of electric power systems. The paper describes a neural network design and simulation environment for real-time fault diagnosis of a model power system.
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