Improved Multi-Grained Cascade Forest Model for Transformer Fault Diagnosis  

作　　者：Yiyi Zhang Yuxuan Wang Jiefeng Liu Heng Zhang Xianhao Fan Dongdong Zhang 

机构地区：[1]Guangxi Key Laboratory of Power System Optimization and Energy Technology,Guangxi University,Nanning,Guangxi,530004,China

出　　处：《CSEE Journal of Power and Energy Systems》2025年第1期468-476,共9页中国电机工程学会电力与能源系统学报(英文)

基　　金：supported in part by the National Natural Science Foundation of China under Grant(52277138);Natural Science Foundation of Guangxi under Grant(2018JJB160064,2018JJA160176)。

摘　　要：Dissolved gas analysis(DGA)is an effective online fault diagnosis technique for large oil-immersed transformers.However,due to the limited number of DGA data,most deep learning models will be overfitted and the classification accuracy cannot be guaranteed.Therefore,this paper has introduced the idea of deep neural networks into the multi-grained cascade forest(gcForest),which is a tree-based deep learning model,and proposed an improved gcForest that can be accelerated by GPU.Firstly,in order to extract features more effectively and reduce memory consumption,the multi-grained scanning of gcForest is replaced by convolutional neural networks.Secondly,the cascade forest(CasForest)is replaced by cascade eXtreme gradient boosting(CasXGBoost)to improve the classification ability.Finally,235 DGA samples are used to train and evaluate the proposed model.The average fault diagnosis accuracy of the improved gcForest is 88.08%,while the average recall,precision,and Fl-score are 0.89,0.90,0.89,respectively.Moreover,the proposed method still has high fault diagnosis accuracy for datasets of different sizes.

关 键 词：Convolutional neural networks dissolved gas analysis fault diagnosis multi-grained cascade forest(gcForest) power transformer 

分 类 号：TM41[电气工程—电器]