Transformer fault diagnosis method based on deep forest model

The invention discloses a transformer fault diagnosis method based on a deep forest model. The method comprises the steps that a non-coding ratio of analysis data of dissolved gas in transformer oil is taken as a characteristic parameter of the deep forest model, and sample data is divided into a training set and a test set; and then a deep forest model DF is constructed, the deep forest model DFextracts more feature information from multi-dimensional data of a transformer fault through multi-granularity scanning, and the effect of diagnosing and identifying the fault type of the transformeris optimal through cascade forest training. According to the method, the fault diagnosis accuracy of the transformer is effectively improved, and a reliable basis is provided for operation and maintenance personnel to correctly judge the operation condition of the transformer. 本发明公开了一种基于深度森林模型的变压器故障诊断方法，首先以变压器油中溶解气体分析数据的无编码比值作为深度森林模型的特征参量，再将样本数据划分为训练集和测试集；然后构建深度森林模型DF，深度森林模型DF通过多粒度扫描对变压器故障的多维数据提取更多特征信息，再经过级联森林的训练达到诊断识别变压器的