On-line fault diagnosis of power substation using connectionist expert system

This paper proposes a new connectionist (or neural network) expert system for the online fault diagnosis of a power substation. The connectionist expert diagnosis system has similar profile to an expert system, but can be constructed much more easily from elemental samples. These samples associate the faults with their protective relays and breakers as well as the bus voltages and feeder currents. Through an elaborately designed structure, alarm signals are processed by different connectionist models. The output of the connectionist models is then integrated to provide fine final conclusion with a confidence level. The proposed approach has been practically verified by testing on a typical Taiwan Power (Taipower) secondary substation. The test results show that rapid and exactly correct diagnosis is obtained even for fault conditions involving multiple faults or failure operation of protective relays and circuit breakers. Moreover, the system can be transplanted into various substations with little additional implementation effort