Fault Diagnosis Of Power Transformer Based On Rough Set Theory And Petri Nets

Power transformer is one of the most important electrical equipments in the electric system, and is also one of the electrical equipments resulting in most electric system accidents. Its operating state affects system's security level directly. It is an important issue for electrical department to find the potential faults of the transformer, to keep it operating safely, and to improve the reliability or power supply. Therefore, it is of great realistic significance to study the fault diagnosis technology of transformer and to increase the operating and maintaining level of transformer.This paper applies the power transformer fault quality diagnosis and fault location diagnosis synchronously.Firstly, research of the relationship between fault types and dissolved gas, a type of 5-12-6 artificial neural network model for transformer fault diagnosis is established. Using of six improved back-propagation network algorithms and fast gradient drop algorithm to train the established artificial neural network, using of transformer data samples to the trained network model and processing fault quality diagnosis. Through the practical fault examples compare the fault diagnosis performance of six improved BP algorithms.Secondly, through rough set theory reduce the attribute of incomplete information table and extract rules, gain the potential and diagnostic rules (if…then…rules), and then based on these rules, the optimum petri nets are built, and through the petri nets calculate fault diagnosis. Rough set theory and petri nets are integrated for fault location diagnosis of transformer.Finally, the correctness and speediness of fault quality diagnosis of transformer based on artificial neural network and fault location diagnosis of transformer based on rough set theory and petri nets are validated by the practical fault examples. Meanwhile, fault diagnosis results can achieve the prospective effects.