A learning system for detecting transformer internal faults

Miniature transformer is one of the most important components of electronic devices. A serious failure of such kind of transformer may cause loss of time and money. This paper presents a learning system to recognize internal fault of such kind of transformer. The different kinds of faults are made to occur intentionally and data are collected at various conditions. The faults include turn to turn, winding to ground, and dielectric faults. The data are then processed and entered in the learning algorithms to recognize the type of fault. We devise a learning system to recognize the various types of faults. Several versions of learning algorithms such as standard back propagation, Levenberg-Marquardt, Bayesian regulation, Resilient back propagation, Gradient descent, One-step secant, Elman recurrent network are used. The result of Levenberg-Marquardt algorithm was found to be faster than that of other algorithms. Therefore it is suitable for real time fault detection.