Application of improved Elman neural network based on fuzzy input for fault diagnosis in oil-filled power transformers

Dissolved Gas Analysis (DGA) is a popular method to detect and diagnose different types of faults occurring in power transformers. In this paper, a improved Elman neural network is used to resolve the online fault diagnosis problems for oil-filled power transformer. Because of the uncertainty factors of the transformer faults ,a method using fuzzy math theory to deal with the data of the neural network input is also proposed. The fault diagnosis structure of neural network based on improved three-ratio method is given. In addition, to improve the convergence speed, Recursive Prediction Error algorithm is used in training network. Through on-line monitoring the concentrations of the dissolved gases, the proposed diagnostic system can offer a way to interpret the incipient faults. The simulation diagnosis demonstrates the effectiveness and veracity of the proposed algorithm.