Fault analysis with modular neural networks

Automatic fault diagnosis in power systems presents real challenges to computing technologies. As an alternative approach to expert systems, several neural network solutions have been proposed recently. In this paper a modular, neural network-based solution to power systems alarm handling and fault diagnosis is described that overcomes the limitations of 'toy' alternatives constrained to small and fixed-topology electrical networks. In contrast to monolithical diagnosis systems, the neural network-based approach presented here fulfills the scalability and dynamic adaptability requirements of the application. Mapping the power grid onto a set of interconnected modules that model the functional behaviour of electrical equipment provides the flexibility and speed demanded by the problem. The way in which the neural system is conceived allows full scalability to real-size power systems.