Fault diagnosis of power system using neural Petri net and fuzzy neural Petri net

In previous study Petri net models were developed for detecting fault location in power system. In the current work, we proposed two models to diagnose the fault: the neural Petri net (NPN) and the fuzzy neural Petri net (FNPN). These models based on underlying Petri net. When the faults occur in power system, it is inevitable that a great amount data transmitted to the control center, but there are some incomplete and uncertain information of protective relays and circuit breaker (CB). Two class of Petri net called the NPN and FNPN, can be used for detection. The final diagnostic report contains information about the location of fault. The developed methodology is tested using an actual power system. Fast and accurate results are obtained