Defining a new method to set certainty factors to improve power systems prognosis with fuzzy petri nets

Abstract

In power systems, faults are unavoidable events. They can cause disastrous problems to operations. In this study, we aim to reduce the bad effects resulting from these faults in order to raise the operation performance. Fuzzy petri nets (FPN) are very important tools used to diagnose and prognosis issues. However, they take constant certainty factors (CFs) and depend upon long-term statistical average values to describe initial places. A new method is introduced here to determine CFs and initial truth degrees in FPN, so they can reflect different operating states and adapt to any changes in conditions in order to improve the prognosis. For this purpose, we define new kinds of CFs in order to take various conditions into account and represent a wide range of their effects on the system. The main purpose of this study is to analyze the system operation at different states under different conditions to determine the state that may cause problems, and take convenient procedures to prevent them. The proposed method is applied on a reliability test system to show its ability to make the FPN model more flexible and cover a wide range of operation cases.