Neural networks for power systems alarm handling

A multi-layered architecture of self-organizing neural networks is being developed as part of an intelligent alarm processor to analyse a stream of power grid fault messages and provide a suggested diagnosis of the fault location. Feedback concerning the accuracy of the diagnosis is provided by an object-oriented grid simulator which acts as an external supervisor to the learning system. The utilization of artificial neural networks within this environment should result in a powerful generic alarm processor which will not require extensive training by a human expert to produce accurate results.

Adaptive alarm processor for fault diagnosis on power transmission networks

The authors describe a learning classifier system (LCS) which employs genetic algorithms (GA) for adaptive online diagnosis of power transmission network faults. The system monitors switchgear indications produced by a transmission network, reporting fault diagnoses on any patterns indicative of faulted components. The system evaluates the accuracy of diagnoses via a fault simulator developed by National Grid Co. and adapts to reflect the current network topology by use of genetic algorithms.