A Fuzzy Petri Net Model Adapts to Changing Operating Conditions to Improve Power Systems Fault Prognosis

Abstract

In power systems operation fast and accurate fault diagnosis and prognosis is important to keep reliability indicators acceptable, as many conditions affect power systems operating it's significant to take their changes in consideration when achieving these procedures. In this paper we present a proposed method to improve fault prognosis using fuzzy petri nets (FPN), by adding internal and external changing conditions to the prognosis process. We aim to study the impact of changing some factors on the state of the system and prognosis the risk that may happened. Usually in FPN certainty factor which describes condition availability degree has been taken constant, that doesn't cover changes in parameters and conditions. We introduce here new kinds of certainty factors can adapt with changed conditions, we applied these suggestions on a bus of reliability test system to show its differences from traditional FPN and discuss results.