Fault Diagnosis of Power Transformer using Duval Triangle Based Artificial Intelligence Techniques

The fault diagnosis of the dissolved gas analysis (DGA) of the power transformer is to be enhanced than previous adopted techniques; this paper proposes a novel adaptive neuro Fuzzy inference system for the incipient fault recognition through enhanced approach. Complying with the practical DGA records and associated fault causes as much as possible, an ANFIS algorithm is presented to establish fault diagnosis system. After 584 groups of training and testing gas-in oil samples, this work compares and analyzes the network training process and diagnostics results of ANFIS with the previous adopted ANN system. The Diagnostics accuracy of the ANFIS is much more accurate than ANN system. The faults are identified for the records using Duval triangle analysis which is more accurate than other Rogers ratio method, IEC codes etc. IndexTerms—Dissolved gas analysis (DGA), fault diagnosis, ANFIS algorithm, power transformers,.