Artificial Neural Network Application in Estimation of Dissolved Gases in Insulating Mineral Oil from Physical-Chemical Datas for Incipient Fault Diagnosis

In this paper, artificial neural networks are used to solve a complex problem concerning to power transformers and characterized by non-linearity and hard dynamic modeling. The operation conditions and integrity of a power transformer can be detected by analysis of physical-chemical and chromatographic isolating oil, allowing establish procedures for operating and maintaining the equipment. However, while the costs of physical-chemical tests are smaller, the chromatographic analysis is more informative. This work presents an estimation study of the information that would be obtained in the chromatographic test from the physical-chemical analysis through artificial neural networks. Thus, the power utilities can achieve greater reliability in the prediction of incipient failures at a lower cost. The results show this strategy to be a promising, with accuracy of 100% in best cases. The application in the thermal fault diagnosis presents more than 91% accuracy in best cases.