Method Based on Rough Set and Improved Petri Net for Transformer Fault Diagnosis

Oil chromatographic analysis is widely used in transformer fault diagnosis, but it is difficult to establish accurate mapping relationships between the parameter space and the state space, and there is information complexity. This paper adopts the combined diagnostic model of rough sets and Petri networks. It first simplifies the complex system that contains complicated discrete information through a rough set to solve the state space limitations of a Petri network and improve the petri network based on mining association rules. It adopts a correlation matrix and state equation method to improve the reasoning speed and, at the same time, turns the diagnosis into matrix operations to change the complex calculations to simple math, which has certain applicability. Finally, the algorithm is applied to gas chromatographic analysis in transformer oil; the calculation results are the same with an IEC three ratio method, which proves that this method can quickly and accurately judge the running state of a transformer to improve the safety, stability and economic operation of a flat water transformer.