Transformer Fault Diagnosis Based on Neural Network of BPARM Algorithm

Slower convergence and longer training time are the disadvantages usually mentioned when the conventional back-propagation (BP) algorithm are utilized in transformer fault diagnosis based on artificial neural network (ANN). Consequently, an efficient acceleration technique- BPARM (back-propagation with adaptive learning rate and momentum term) algorithm was proposed to reduce the training time, where the learning rate and the momentum term are altered at iteration. We implemented a system of transformer fault diagnosis based on dissolved gases analysis (DGA) with BPARM. Training patterns were extracted from refined three-ratio method. Test results show that the system has the better ability of quick learning and global convergence than other methods, and improves accuracy of fault recognition