A neural network approach to power transformer fault diagnosis

Diagnosis of power transformer abnormality is important for power system reliability. This paper introduces the dissolved gas-in-oil analysis (DGA) according to the characteristic of transformer fault diagnosis, based on fuzzy set theory and adaptive genetic algorithm, a neural network model for transformer fault diagnosis is built by using modular back-propagation (BP). The results of training and testing show that the method is effective and available.