Dissolved Gas Analysis of Insulating Oil in Electric Power Transformers: A Case Study Using SDAE-LSTM

Abstract

Dissolved gas analysis (DGA) is the most important tool for fault diagnosis in electric power transformers. To improve accuracy of diagnosis, this paper proposed a new model (SDAE-LSTM) to identify the dissolved gases in the insulating oil of power transformers and perform parameter analysis. The performance evaluation is attained by the case studies in terms of recognition accuracy, precision ratio, and recall ratio. Experiment results show that the SDAE-LSTM model performs better than other models under different input conditions. As evidenced from the analyses, the proposed model achieves considerable results of recognition accuracy (95.86%), precision ratio (95.79%), and recall ratio (97.51%). It can be confirmed that the SDAE-LSTM model using the dissolved gas in the power transformer for fault diagnosis and analysis has great research prospect.