A Systematic Stochastic Petri Net Based Methodology for Transformer Fault Diagnosis and Repair Actions

Abstract

Transformer fault diagnosis and repair is a complex task that includes many possible types of faults and demands special trained personnel. Moreover, the minimization of the time needed for transformer fault diagnosis and repair is an important task for electric utilities, especially in cases where the continuity of supply is crucial. In this paper, Stochastic Petri Nets are used for the simulation of the fault diagnosis process of oil-immersed transformers and the definition of the actions followed to repair the transformer. Transformer fault detection is realized using an integrated safety detector, in case of sealed type transformer that is completely filled with oil, while a Buchholz relay and an oil thermometer are used, in case of transformer with conservator tank. Simulation results for the most common types of transformer faults (overloading, oil leakage, short-circuit and insulation failure) are presented. The proposed Stochastic Petri Net based methodology provides a systematical determination of the sequence of fault diagnosis and repair actions and aims at identifying the transformer fault and estimating the duration for transformer repair.