Identifying the primary fault section after contingencies in bulk power systems

This paper deals with the problem of fault section estimation in electric power systems, undertaken at a control center level and using information about the operation of protection relays and circuit breakers. The developed methodology should be used after the occurrence of contingencies with definitive disconnections, and before beginning the process of network restoration. Due to the absence of an analytic formulation, the problem calls for the use of artificial-intelligence techniques, such as neural networks and expert systems. Neural networks are employed to model the protection systems, dealing with the uncertainties involved with relay and circuit-breaker operation messages. An expert system is used to complement the results provided by the neural networks, considering the network topology. The results show that the developed methodology is applicable to real large-scale power systems. In addition, it is capable of noise suppression in relay and circuit-breaker trip messages, treats multiple faults naturally, and infers a solution even in cases when remote backup protection action occurs.