A cognitive system for fault prognosis in power transformers.

Abstract

The power transformer is one of the most critical and expensive equipments in an electric power system.If it is out of service in an unexpected way, the damage for both society and electric utilities is verysignificant. Over the last decades, many computational tools have been developed to monitor the ‘health’of such an important equipment. The classification of incipient faults in power transformers via DissolvedGas Analysis (DGA) is, for instance, a very well known technique for this purpose. In this paper we presentan intelligent system based on cognitive systems for fault prognosis in power transformers. The proposedsystem combines both evolutionary and connectionist mechanisms into a hybrid model that can bean essential tool in the development of a predictive maintenance technology, to anticipate when anyequipment fault might occur and to prevent or reduce unplanned reactive maintenance. The proposedprocedure has been applied to real databases derived from chromatographic tests of power transformersfound in the literature. The obtained results are fully described showing the feasibility and validity ofthe new methodology. The proposed system can help Transformer Predictive Maintenance programmesoffering a low cost and highly flexible solution for fault prognosis.