Power Transformers Diagnosis Using Neural Networks

Abstract

Power transformers are one of the most used and expensive equipments in many substations of electric energy. This fact justifies the application of predictive techniques of diagnosis, with the objective to minimize possible failures and to increase the trustworthiness of the system. Amongst these techniques, one of the most distinguished are the analysis of gases dissolved in the oil (gaseous chromatography) and the physical-chemical analysis of the isolating oil. Although their generalized use, the diagnosis made by these techniques presents deficiencies, demanding the presence of specialists to complete the diagnosis. A great contribution for the electric sector would be a decision support tool capable of providing a correct and automatic diagnosis, to improve the monitoring process of power transformers. This article presents a diagnosis system, based on two artificial neural networks, each dedicated to the analysis of gaseous chromatography and physical-chemical of the isolating oil, respectively. The idea to enclose these two techniques is to accomplish a more complete diagnosis of the equipment, as well as a reduction of specialists' participation, creating a more automatic diagnosis system. The obtained results with the proposed system are compared with traditional methods. The resultant system represents a more complete decision support tool in the determination of the diagnosis of power transformers.