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

Adistribution transformer is one of the most expensive pieces of equipment in an electricity system. The condition of a distribution transformer is crucial for its successful operation and, as a consequence, for the reliability of the distribution system as whole. The detection of incipient faults which may be caused by insulation weakness, malfunction, defects or deterioration is of fundamental importance. Monitoring the performance of a transformer is crucial in minimizing distribution outages through appropriate maintenance thereby reducing the total cost of operation. 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.