Multi-filter semi-supervised transformer model for fault diagnosis

Abstract

Dissolved Gas Analysis (DGA) is the most commonly used method for power transformer fault diagnosis. However, very few reliable and labeled fault DGA samples are available in the transformer substation whilst DGA data without labels is easier to obtain, which makes it difficult to train fault detectors in high-dimensional input space or select features using wrapper methods. Therefore, in order to improve the fault diagnosis accuracy using limited labeled DGA samples but more unlabeled DGA data, this paper proposes a novel multi-filter semi-supervised feature selection method for selecting optimal DGA features and building effective fault diagnosis models. A confidence criterion is also proposed for selecting high confidence unlabeled data to expand the training data set. Five filter techniques based on different evaluation criteria are employed to rank input DGA features, and a feature combination method is then applied to aggregate feature ranks by multiple filters and form a lower-dimensional candidate feature subset. The proposed method has been tested by using the IEC T10 dataset and compared with traditional supervised diagnostic models. The results show that the proposed method works well in optimizing DGA features and improving fault diagnosis accuracy significantly. Besides, the robustness of the selection of optimal feature subset is validated by testing DGA samples from the local power utility.