Prediction of Fault in Distribution Transformer using Adaptive Neural-Fuzzy Interference System

This presents a new method for simultaneous diagnosis of fault in distribution transformer. It uses an adaptive neuro-fuzzy inference system (ANFIS), based on Dissolved Gas Analysis (DGA). The ANFIS is first “trained� in accordance with IEC 599, so that it acquires some fault determination ability. The CO2/CO ratios are then considered additional input data, enabling simultaneous diagnosis of the type and location of the fault. Diagnosis techniques based on the Dissolved Gas Analysis (DGA) have been developed to detect incipient faults in distribution transformers. The quantity of the dissolved gas depends fundamentally on the types of faults occurring within distribution transformers. By considering these characteristics, Dissolved Gas Analysis (DGA) methods make it possible to detect the abnormality of the transformers. This can be done by comparing the Dissolved Gas Analysis (DGA) of the transformer under surveillance with the standard one. This idea provides the use of adaptive neural fuzzy technique in order to better predict oil conditions of a transformer. The proposed method can forecast the possible faults which can be occurred in the transformer. This idea can be used for maintenance purpose in the technology where distributed transformer plays a significant role such as when the energy is to be distributed in a large region.