Title :
Neural networks for fault location in substations
Author :
Da Silva, A. P Alves ; Insfran, A.H.F. ; da Silveira, P.M. ; Torres, G. Lambert
Author_Institution :
Escola Federal de Engenharia de Itajuba, Brazil
fDate :
1/1/1996 12:00:00 AM
Abstract :
Faults producing load disconnections or emergency situations have to be located as soon as possible to start the electric network reconfiguration, restoring normal energy supply. This paper proposes the use of artificial neural networks (ANNs), of the associative memory type, to solve the fault location problem. The main idea is to store measurement sets representing the normal behavior of the protection system, considering the basic substation topology only, into associative memories. Afterwards, these memories are employed on-line for fault location using the protection system equipment status. The associative memories work correctly even in case of malfunction of the protection system and different pre-fault configurations. Although the ANNs are trained with single contingencies only, their generalization capability allows a good performance for multiple contingencies. The resultant fault location system is in operation at the 500 kV gas-insulated substation of the Itaipu system
Keywords :
content-addressable storage; fault diagnosis; fault location; gas insulated substations; generalisation (artificial intelligence); neural nets; power system analysis computing; 500 kV; Itaipu system; associative memory; electric network reconfiguration; emergency situations; energy supply restoration; fault location; gas-insulated substation; generalization capability; load disconnections; neural networks; pre-fault configurations; protection system equipment status; substation topology; substations; Artificial neural networks; Associative memory; Circuit faults; Fault diagnosis; Fault location; Intelligent networks; Neural networks; Power system protection; Power system restoration; Substation protection;
Journal_Title :
Power Delivery, IEEE Transactions on
