Study on Chromatographic Condition Assessment of Transformer Oil Based on Random Forest Model

In order to solve the problem that the accuracy of transformer condition assessment based on oil chromatographic data is not high, this paper adopts random forest to evaluate transformer state. In order to prevent the attribute values carried by the decision tree of the basic unit in the evaluation model from being too few and thus affecting the evaluation accuracy. The ratio of H2, CH4, C2H6, C2H4, C2H2, total hydrocarbon and various characteristic gases is used as the characteristic quantity, a stochastic forest optimization model for oil chromatography state assessment was constructedimmediately following the heading. It is compared with BP neural network and SVM, the test results of field oil chromatogram data show that the accuracy of random forest state assessment is 88.5%, which is better than BP and SVM, and the random forest assessment model has faster assessment speed, This paper provides technical support for the fast and accurate condition assessment of transformer.