Title :
Adaptive alarm processor for fault diagnosis on power transmission networks
Author :
Kiernan, L. ; Warwick, K.
Author_Institution :
Dept. of Cybern., Reading Univ., UK
Abstract :
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
Keywords :
alarm systems; diagnostic expert systems; fault location; genetic algorithms; learning (artificial intelligence); power system analysis computing; transmission networks; National Grid Co.; UK; adaptive alarm processor; adaptive online diagnosis; fault diagnoses; fault diagnosis; genetic algorithms; learning classifier system; network topology; power transmission network faults; switchgear indication monitoring;
Journal_Title :
Intelligent Systems Engineering
