Transformer fault diagnosis method based on rough set and generalized distribution table

Transformers are considered as significant equipments in electrical power systems, once failure, the economic operation will be lost. To overcome this difficulty and to maintain economic operation of facilities, diverse diagnosis methods are developed to implement fault forecasting. According to intelligent complementary ideas, a fault diagnosis is proposed when there is a missing failure symptom of transformer. The core of the proposed approach is a soft hybrid induction system called the Generalized Distribution Table and Rough Set System (GDT-RS) to discover classification rules. The system is based on a combination of Generalized Distribution Table (GDT) and the Rough Set methodologies. The proposed approach is applied into transformer fault diagnosis and the results indicate that it is very effective and accurate.