Transformer Dissolved Gas Analysis for Highly-Imbalanced Dataset Using Multiclass Sequential Ensembled ELM

Dissolved gas analysis (DGA) has been a critical technique for transformer diagnosis. DGA is a typical multiclass imbalance problem where most of the samples correspond to healthy state transformers or units. Though numerous works have been carried out on this issue, the diagnosis accuracy is still unsatisfactory when the status of health and multiple faults are considered. Multiclass imbalance problem is also a tough task from the view of algorithm development. Previous works underestimate this issue in some sakes such as lacking investigation of the highly imbalanced dataset and lacking consideration of health data. This article presents a comprehensive study of the mentioned issues. A novel algorithm called sequential ensembled extreme learning machine (SE-ELM) is proposed. SE-ELM adopts a novel multiclass undersampling strategy followed by a sequentially updated ensemble, which achieves both accuracy and efficiency. The proposed method is validated on both an open international electrotechnical commission (IEC) dataset and a highly imbalanced private dataset. The comparison with popular algorithms proves the efficiency of SE-ELM.