Extração de regras interpretáveis para o diagnóstico eficiente de transformadores de potência isolados a óleo a partir do aumento do espaço de atributos

Abstract

This work proposes a new method for diagnosis of incipient faults in power equipment with insulation oil. It consists on a set of interpretable rules extracted from the concentrations of the dissolved gases in insulating oil, and the relationships between these concentrations. Using a Decision Tree (DT) with changes in the size of the input attributes for obtaining rules, the proposed method: (i) allows the user to know the rules that generated the diagnosis, (ii) bypasses limitations common to other methods (e.g. the existence of "no diagnosis" regions, and the occurrence of more than one possible diagnosis) and (iii) the accuracy in diagnosis of DGA (Dissolved Gas Analysis) data available in the literature. The proposed method is compared with other DGA standard methods: Gas Key, Rogers, Dornenburg and Duval´s Triangle. The performed comparison considered databases available in technical literature, including data from the IEC TC 10. The observed accuracy of the proposed method is above 94%. Comparatively, the Duval´s Triangle method presents accuracy between 83% and 88% for the considered databases. The method was also evaluated with respect to sensitivity to measurements errors of laboratory – the results are very promising, the proposed method shows a hit rate of 83% in comparison with a hit rate of 78% for the Duval´s Triangle method. This work has also been developed a method for simplified diagnostic of power equipments with insulated oil using physico-chemical data - the hit rates obtained were lower than those achieved with the proposed DGA, however close to those observed in DGA methods cited in the literature, as Rogers IEEE C57.104 - 2008, with the advantage of the data not depend on the availability of a gas chromatograph.