Improved Multi-Grained Cascade Forest Model for Transformer Fault Diagnosis

Abstract

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.

Keywords

• Convolutional neural networks
• dissolved gas analysis
• fault diagnosis
• multi-grained cascade forest (gcForest)
• power transformer