Transformer fault text classification model based on double feature channel

Abstract

The power transformer plays a key role in the power distribution and transmission of the power system of the equipment, if the transformer fault leads to unexpected outage of the power system, then it will have a great threat to the whole power system, through the analysis of the transformer fault text can better guide the procurement of the transformer and fault maintenance. This paper proposes a classification method of transformer fault text based on two feature channels. This method extracts useful text emotion information from different aspects and levels by using two feature vectors, namely word vector and pinyin vector, and inputs it into Bilstm network to extract local and global semantic features. Then the features are fused through the attention mechanism. Finally, the features output by softmax function are classified and predicted. Experimental results show that the proposed method greatly improves the accuracy of transformer fault text classification.