Application of an Artificial Neural Network in the Use of Physicochemical Properties as a Low Cost Proxy of Power Transformers DGA Data

This paper is about the relationship between dissolved gases and the quality of the insulating mineral oil used in power transformers. Artificial Neural Networks are used to approach operational conditions assessment issue of the insulating oil in power transformers, which is characterized by a non-linear dynamic behavior. The operation conditions and integrity of a power transformer can be inferred by analysis of physicochemical and chromatographic (DGA - Dissolved Gas Analysis) profiles of the isolating oil, which allow establishing procedures for operating and maintaining the equipment. However, while the costs of physicochemical tests are less expensive, the chromatographic analysis is more informative and reliable. This work presents a method that can be used to extract chromatographic information using physicochemical analysis through Artificial Neural Networks. It's believed that, the power utilities could improve reliability in the prediction of incipient failures at a lower cost with this method. The results show this strategy might be promising. The purpose of this work is the direct implementation of the diagnosis of incipient faults through the use of physicochemical properties without the need to make an oil chromatography.