Rough Set Theory Applied Research In Fault Diagnosis Of Transformer

Because the transformer is one of the most important equipments in Power System, the technology of fault diagnosis for transformer is always taken into account by savants all over the world. The theory and application of Rough Set in fault diagnosis of transformer has been systematically studied in this paper, which includes: Basic concept and the developing situation in application field of fault diagnosis have been summarized; relative theory of RS has been introduced, and the applications in determination and fault diagnosis has been discussed; Based on the generally typical faults, a variety of inspect and fault diagnosis technology has been mentioned, and the course of robot system which is the combination of RS and Neural Network has been presented; the results of simulation on transformer diagnosis show some effect, but the defects are also existed; accordingly the neural network which based on RS theory is applied in transformer fault diagnosis. It fully develops the reduction ability of RS theory and the classification ability of neural network. Using RS theory, the original data reduction is performed first to form a simple rule collection, based on which the structure of neural network is then completely determined. It has a better topological structure with greatly reduce network scale and greatly improved learning speed, while keeps the good classification ability of network. The results of simulation show that this method has high fault-tolerance performance and it is effective.